Slicer Wiki - User contributions [en]

Stanford Simbios group

2009-05-30T02:48:17Z

Harishd:

<gallery widths="400px" perrow="6">
Image:58.jpg
Image:All_Three.JPG
Image:Femur_Patella_Tibia.jpg
</gallery>

=Aim=
To develop a generic semi-automatic segmentation toolkit to convert images of musculoskeletal structures to 3D models.

=Process Flowchart=
[[Image:FlowChart.jpg|left| Process Diagram.]]

=Progress=
==Atlas Generation from Input MR Images==
===Model Generation from Input MR Images===
Pre-Segmented models for femur, patella and tibia are obtained for a patient (in .stl format).
[[Image:All_Three.JPG|left|thumb| Pre-Segmented Femur/Patella/Tibia Model]]

===Create a filled label map using PolyDataToFilledLabelMap module in slicer===
The models generated in the above step are closed but hollow. But EM Segmentation requires the atlas given to be in closed filled format. Hence we converted the hollow closed models into filled label volumes using the module PolyDataToFilledLabelMap module in Slicer. The output label map of this module is of datatype unsigned char and is converted to short datatype.
<gallery widths="400px" perrow="6">
Image:LabelMap_Femur.jpg
Image:LabelMap_Tibia.jpg
Image:LabelMap_Patella.jpg
</gallery>

===EM Segmentation based on the atlas===
The output label map in the above is given as input atlas in the EM Segmentation step. As an initial step we ran EM Segmentation on the same patient. The EM Segmented output is given in the below figure.
[[Image:Femur_Patella_Tibia.jpg|left|thumb| EM Segmented Output]]
==Register Images==
===Register Images Module in Slicer===
We are now in the process of trying to register new patient image to the existing patient image. If we register the images, we can get the transform which can be used to register the existing atlas (label map) to the new patient.

We think that Pipelined Bspline Registration may give promising results for our dataset. Below are some of the results. Here we consider 64_MRI as the fixed image and 58_MRI as the moving image and used '''Pipelined BSpline Image registration method''' for registration. Also we gave one point as landmark point.
<gallery widths="400px" perrow="6">
Image:58_MRI.jpg
Image:64_MRI.jpg
Image:64_Registered.jpg
</gallery>

We also tried '''affine registration''' for the above mentioned patients with 64 as fixed MRI and 58 as moving MRI and '''one landmark point'''. The zipped file which contains scene along with screenshots is
[[Image:64 58 Affine Registration.zip |left|64_Affine_Registered_Scene]]
<gallery widths="400px" perrow="6">
Image:64 Affine Registration.JPG
Image:64 Registerd SuperImposed On 64 Actual.JPG
</gallery>

We also tried '''BSpline registration''' for the above mentioned patients with 64 as fixed MRI and 58 as moving MRI and one landmark point. The zipped file which contains scene along with screenshots is
[[Image:64 58 BSpline Registration.zip |left|64_BSpline_Registered_Scene]]. Here we used '''4 points''' as landmarks.
<gallery widths="400px" perrow="6">
Image:64 BSpline Registration.JPG
Image:64 BSpline Registered SuperImposed On 64 Actual.JPG
</gallery>

We also tried '''Affine registration''' for the above mentioned patients with 64 as fixed MRI and 58 as moving MRI and four landmark point. The zipped file which contains scene along with screenshots is
[[Image:64_58_Affine_Various_Iterations.zip |left|64_Affine_Registered_Various_Iterations_Scene]]. Here we used '''4 points''' as landmarks. We varied the number of iterations from 200 to 400.
<gallery widths="400px" perrow="6">
Image:64 58 Affine 200 Iterations.JPG
Image:64 58 Affine 300 Iterations.JPG
Image:64 58 Affine 400 Iterations.JPG
</gallery>

==='''Update May 13, 2009'''===
We tried '''Affine registration''' for the above mentioned patients with 64 as fixed MRI and 58 as moving MRI and '''no''' landmark point. The zipped file which contains scene along with screenshots is
[[Image:64_58_Affine_Various_Iterations.zip |left|64_Affine_Registered_Various_Iterations_Scene]]. We varied the number of iterations from 200 to 400.
<gallery widths="400px" perrow="6">
Image:64 58 Affine 200 Iterations.JPG
Image:64 58 Affine 300 Iterations.JPG
Image:64 58 Affine 400 Iterations.JPG
</gallery>

==='''Update May 14, 2009'''===
We tried '''Affine registration''' for the above mentioned patients with 64 as fixed MRI and 58 as moving MRI and '''10''' landmark point. The zipped file which contains scene along with screenshots is
[[Image:Affine Registration 10 Landmark Points Varying Iterations.zip |left|64_Affine_Registered_10_Landmarks_Various_Iterations_Scene]]. We varied the number of iterations from 50 to 450.
<gallery widths="200px" perrow="6">
Image:64 58 Affine 50 Iterations 10 Landmark Points.jpg
Image:64 58 Affine 100 Iterations 10 Landmark Points.jpg
Image:64 58 Affine 150 Iterations 10 Landmark Points.jpg
Image:64 58 Affine 250 Iterations 10 Landmark Points.jpg
Image:64 58 Affine 350 Iterations 10 Landmark Points.jpg
Image:64 58 Affine 450 Iterations 10 Landmark Points.jpg
</gallery>

==='''Update May 20, 2009'''===
We tried '''BSpline registration''' for the above mentioned patients with 64 as fixed MRI and 58 as moving MRI and '''no''' landmark point. The zipped file which contains scene along with screenshots is
[[Image:Affine Registration 10 Landmark Points Varying Iterations.zip |left|64_Affine_Registered_10_Landmarks_Various_Iterations_Scene]]. We varied the number of iterations from 50 to 450.
<gallery widths="200px" perrow="6">
Image:64 58 BSpline 10 Iterations.JPG
Image:64 58 BSpline 110 Iterations.JPG
Image:64 58 BSpline 160 Iterations.JPG
Image:64 58 BSpline 210 Iterations.JPG
Image:64 58 BSpline 260 Iterations.JPG
Image:64 58 BSpline 360 Iterations.JPG
</gallery>

==='''Update May 21, 2009'''===
We tried '''Pipelined Affine registration''' for the above mentioned patients with 64 as fixed MRI and 58 as moving MRI and '''no''' landmark point. The zipped file which contains scene along with screenshots is
[[Image:BSpline_Registration_Varying_Iterations.zip‎ |left|BSpline_Registration_Varying_Iterations]]. We varied the number of iterations from 50 to 450.
<gallery widths="200px" perrow="6">
Image:64 58 PipeLinedAffine Registration 50 Iterations.JPG
Image:64 58 PipeLinedAffine Registration 100 Iterations.JPG
Image:64 58 PipeLinedAffine Registration 150 Iterations.JPG
Image:64 58 PipeLinedAffine Registration 200 Iterations.JPG
Image:64 58 PipeLinedAffine Registration 300 Iterations.JPG
Image:64 58 PipeLinedAffine Registration 400 Iterations.JPG
</gallery>

==='''Update May 25, 2009'''===
We tried '''Affine registration''' for the above mentioned patients with 64 as fixed MRI and 58 as moving MRI and '''no''' landmark point. The zipped file which contains scene along with screenshots is
[[Image:BSpline_Registration_Varying_Iterations.zip‎ |left|BSpline_Registration_Varying_Iterations]]. We varied the number of iterations from 50 to 5000.
<gallery widths="200px" perrow="6">
Image:AffineRegistration InitialTransform 200Iterations.JPG
Image:AffineRegistration InitialTransform 300Iterations.JPG
Image:AffineRegistration InitialTransform 400Iterations.JPG
</gallery>

===Multi Image Registration===
We have also tried the approach of Non-rigid Groupwise Registration using Bspline Deformation Model by Serdar K. Balci, Polina Golland and William M. Wells. In this approach, we try to give one slice each of two different patients we need to register as input. Here are some of the results of their approach.

<gallery widths="200px" perrow="2">
Slice 1:
Image:1_Slicez0.JPG‎
Slice 2:
Image:1_Slicez1.JPG
Mean Slice of above two :
Image:1_MeanSliceZ.JPG
</gallery>

<gallery widths="200px" perrow="2">
Slice 1:
Image:2_Z0.JPG
Slice 2:
Image:2_Z1.JPG
Mean Slice of above two :
Image:2_MeanSliceZ.JPG
</gallery>

File:AffineRegistration InitialTransform 400Iterations.JPG

2009-05-30T02:48:02Z

Harishd:

File:AffineRegistration InitialTransform 300Iterations.JPG

2009-05-30T02:47:49Z

Harishd:

File:AffineRegistration InitialTransform 200Iterations.JPG

2009-05-30T02:46:26Z

Harishd:

Stanford Simbios group

2009-05-20T23:35:30Z

Harishd:

<gallery widths="400px" perrow="6">
Image:58.jpg
Image:All_Three.JPG
Image:Femur_Patella_Tibia.jpg
</gallery>

=Aim=
To develop a generic semi-automatic segmentation toolkit to convert images of musculoskeletal structures to 3D models.

=Process Flowchart=
[[Image:FlowChart.jpg|left| Process Diagram.]]

=Progress=
==Atlas Generation from Input MR Images==
===Model Generation from Input MR Images===
Pre-Segmented models for femur, patella and tibia are obtained for a patient (in .stl format).
[[Image:All_Three.JPG|left|thumb| Pre-Segmented Femur/Patella/Tibia Model]]

===Create a filled label map using PolyDataToFilledLabelMap module in slicer===
The models generated in the above step are closed but hollow. But EM Segmentation requires the atlas given to be in closed filled format. Hence we converted the hollow closed models into filled label volumes using the module PolyDataToFilledLabelMap module in Slicer. The output label map of this module is of datatype unsigned char and is converted to short datatype.
<gallery widths="400px" perrow="6">
Image:LabelMap_Femur.jpg
Image:LabelMap_Tibia.jpg
Image:LabelMap_Patella.jpg
</gallery>

===EM Segmentation based on the atlas===
The output label map in the above is given as input atlas in the EM Segmentation step. As an initial step we ran EM Segmentation on the same patient. The EM Segmented output is given in the below figure.
[[Image:Femur_Patella_Tibia.jpg|left|thumb| EM Segmented Output]]
==Register Images==
===Register Images Module in Slicer===
We are now in the process of trying to register new patient image to the existing patient image. If we register the images, we can get the transform which can be used to register the existing atlas (label map) to the new patient.

We think that Pipelined Bspline Registration may give promising results for our dataset. Below are some of the results. Here we consider 64_MRI as the fixed image and 58_MRI as the moving image and used '''Pipelined BSpline Image registration method''' for registration. Also we gave one point as landmark point.
<gallery widths="400px" perrow="6">
Image:58_MRI.jpg
Image:64_MRI.jpg
Image:64_Registered.jpg
</gallery>

We also tried '''affine registration''' for the above mentioned patients with 64 as fixed MRI and 58 as moving MRI and '''one landmark point'''. The zipped file which contains scene along with screenshots is
[[Image:64 58 Affine Registration.zip |left|64_Affine_Registered_Scene]]
<gallery widths="400px" perrow="6">
Image:64 Affine Registration.JPG
Image:64 Registerd SuperImposed On 64 Actual.JPG
</gallery>

We also tried '''BSpline registration''' for the above mentioned patients with 64 as fixed MRI and 58 as moving MRI and one landmark point. The zipped file which contains scene along with screenshots is
[[Image:64 58 BSpline Registration.zip |left|64_BSpline_Registered_Scene]]. Here we used '''4 points''' as landmarks.
<gallery widths="400px" perrow="6">
Image:64 BSpline Registration.JPG
Image:64 BSpline Registered SuperImposed On 64 Actual.JPG
</gallery>

We also tried '''Affine registration''' for the above mentioned patients with 64 as fixed MRI and 58 as moving MRI and four landmark point. The zipped file which contains scene along with screenshots is
[[Image:64_58_Affine_Various_Iterations.zip |left|64_Affine_Registered_Various_Iterations_Scene]]. Here we used '''4 points''' as landmarks. We varied the number of iterations from 200 to 400.
<gallery widths="400px" perrow="6">
Image:64 58 Affine 200 Iterations.JPG
Image:64 58 Affine 300 Iterations.JPG
Image:64 58 Affine 400 Iterations.JPG
</gallery>

==='''Update May 13, 2009'''===
We tried '''Affine registration''' for the above mentioned patients with 64 as fixed MRI and 58 as moving MRI and '''no''' landmark point. The zipped file which contains scene along with screenshots is
[[Image:64_58_Affine_Various_Iterations.zip |left|64_Affine_Registered_Various_Iterations_Scene]]. We varied the number of iterations from 200 to 400.
<gallery widths="400px" perrow="6">
Image:64 58 Affine 200 Iterations.JPG
Image:64 58 Affine 300 Iterations.JPG
Image:64 58 Affine 400 Iterations.JPG
</gallery>

==='''Update May 14, 2009'''===
We tried '''Affine registration''' for the above mentioned patients with 64 as fixed MRI and 58 as moving MRI and '''10''' landmark point. The zipped file which contains scene along with screenshots is
[[Image:Affine Registration 10 Landmark Points Varying Iterations.zip |left|64_Affine_Registered_10_Landmarks_Various_Iterations_Scene]]. We varied the number of iterations from 50 to 450.
<gallery widths="200px" perrow="6">
Image:64 58 Affine 50 Iterations 10 Landmark Points.jpg
Image:64 58 Affine 100 Iterations 10 Landmark Points.jpg
Image:64 58 Affine 150 Iterations 10 Landmark Points.jpg
Image:64 58 Affine 250 Iterations 10 Landmark Points.jpg
Image:64 58 Affine 350 Iterations 10 Landmark Points.jpg
Image:64 58 Affine 450 Iterations 10 Landmark Points.jpg
</gallery>

==='''Update May 20, 2009'''===
We tried '''BSpline registration''' for the above mentioned patients with 64 as fixed MRI and 58 as moving MRI and '''no''' landmark point. The zipped file which contains scene along with screenshots is
[[Image:Affine Registration 10 Landmark Points Varying Iterations.zip |left|64_Affine_Registered_10_Landmarks_Various_Iterations_Scene]]. We varied the number of iterations from 50 to 450.
<gallery widths="200px" perrow="6">
Image:64 58 BSpline 10 Iterations.JPG
Image:64 58 BSpline 110 Iterations.JPG
Image:64 58 BSpline 160 Iterations.JPG
Image:64 58 BSpline 210 Iterations.JPG
Image:64 58 BSpline 260 Iterations.JPG
Image:64 58 BSpline 360 Iterations.JPG
</gallery>

==='''Update May 21, 2009'''===
We tried '''Pipelined Affine registration''' for the above mentioned patients with 64 as fixed MRI and 58 as moving MRI and '''no''' landmark point. The zipped file which contains scene along with screenshots is
[[Image:BSpline_Registration_Varying_Iterations.zip‎ |left|BSpline_Registration_Varying_Iterations]]. We varied the number of iterations from 50 to 450.
<gallery widths="200px" perrow="6">
Image:64 58 PipeLinedAffine Registration 50 Iterations.JPG
Image:64 58 PipeLinedAffine Registration 100 Iterations.JPG
Image:64 58 PipeLinedAffine Registration 150 Iterations.JPG
Image:64 58 PipeLinedAffine Registration 200 Iterations.JPG
Image:64 58 PipeLinedAffine Registration 300 Iterations.JPG
Image:64 58 PipeLinedAffine Registration 400 Iterations.JPG
</gallery>

===Multi Image Registration===
We have also tried the approach of Non-rigid Groupwise Registration using Bspline Deformation Model by Serdar K. Balci, Polina Golland and William M. Wells. In this approach, we try to give one slice each of two different patients we need to register as input. Here are some of the results of their approach.

<gallery widths="200px" perrow="2">
Slice 1:
Image:1_Slicez0.JPG‎
Slice 2:
Image:1_Slicez1.JPG
Mean Slice of above two :
Image:1_MeanSliceZ.JPG
</gallery>

<gallery widths="200px" perrow="2">
Slice 1:
Image:2_Z0.JPG
Slice 2:
Image:2_Z1.JPG
Mean Slice of above two :
Image:2_MeanSliceZ.JPG
</gallery>

File:BSpline Registration Varying Iterations.zip

2009-05-20T23:34:21Z

Harishd:

Stanford Simbios group

2009-05-20T19:40:53Z

Harishd:

<gallery widths="400px" perrow="6">
Image:58.jpg
Image:All_Three.JPG
Image:Femur_Patella_Tibia.jpg
</gallery>

=Aim=
To develop a generic semi-automatic segmentation toolkit to convert images of musculoskeletal structures to 3D models.

=Process Flowchart=
[[Image:FlowChart.jpg|left| Process Diagram.]]

=Progress=
==Atlas Generation from Input MR Images==
===Model Generation from Input MR Images===
Pre-Segmented models for femur, patella and tibia are obtained for a patient (in .stl format).
[[Image:All_Three.JPG|left|thumb| Pre-Segmented Femur/Patella/Tibia Model]]

===Create a filled label map using PolyDataToFilledLabelMap module in slicer===
The models generated in the above step are closed but hollow. But EM Segmentation requires the atlas given to be in closed filled format. Hence we converted the hollow closed models into filled label volumes using the module PolyDataToFilledLabelMap module in Slicer. The output label map of this module is of datatype unsigned char and is converted to short datatype.
<gallery widths="400px" perrow="6">
Image:LabelMap_Femur.jpg
Image:LabelMap_Tibia.jpg
Image:LabelMap_Patella.jpg
</gallery>

===EM Segmentation based on the atlas===
The output label map in the above is given as input atlas in the EM Segmentation step. As an initial step we ran EM Segmentation on the same patient. The EM Segmented output is given in the below figure.
[[Image:Femur_Patella_Tibia.jpg|left|thumb| EM Segmented Output]]
==Register Images==
===Register Images Module in Slicer===
We are now in the process of trying to register new patient image to the existing patient image. If we register the images, we can get the transform which can be used to register the existing atlas (label map) to the new patient.

We think that Pipelined Bspline Registration may give promising results for our dataset. Below are some of the results. Here we consider 64_MRI as the fixed image and 58_MRI as the moving image and used '''Pipelined BSpline Image registration method''' for registration. Also we gave one point as landmark point.
<gallery widths="400px" perrow="6">
Image:58_MRI.jpg
Image:64_MRI.jpg
Image:64_Registered.jpg
</gallery>

We also tried '''affine registration''' for the above mentioned patients with 64 as fixed MRI and 58 as moving MRI and '''one landmark point'''. The zipped file which contains scene along with screenshots is
[[Image:64 58 Affine Registration.zip |left|64_Affine_Registered_Scene]]
<gallery widths="400px" perrow="6">
Image:64 Affine Registration.JPG
Image:64 Registerd SuperImposed On 64 Actual.JPG
</gallery>

We also tried '''BSpline registration''' for the above mentioned patients with 64 as fixed MRI and 58 as moving MRI and one landmark point. The zipped file which contains scene along with screenshots is
[[Image:64 58 BSpline Registration.zip |left|64_BSpline_Registered_Scene]]. Here we used '''4 points''' as landmarks.
<gallery widths="400px" perrow="6">
Image:64 BSpline Registration.JPG
Image:64 BSpline Registered SuperImposed On 64 Actual.JPG
</gallery>

We also tried '''Affine registration''' for the above mentioned patients with 64 as fixed MRI and 58 as moving MRI and four landmark point. The zipped file which contains scene along with screenshots is
[[Image:64_58_Affine_Various_Iterations.zip |left|64_Affine_Registered_Various_Iterations_Scene]]. Here we used '''4 points''' as landmarks. We varied the number of iterations from 200 to 400.
<gallery widths="400px" perrow="6">
Image:64 58 Affine 200 Iterations.JPG
Image:64 58 Affine 300 Iterations.JPG
Image:64 58 Affine 400 Iterations.JPG
</gallery>

==='''Update May 13, 2009'''===
We tried '''Affine registration''' for the above mentioned patients with 64 as fixed MRI and 58 as moving MRI and '''no''' landmark point. The zipped file which contains scene along with screenshots is
[[Image:64_58_Affine_Various_Iterations.zip |left|64_Affine_Registered_Various_Iterations_Scene]]. We varied the number of iterations from 200 to 400.
<gallery widths="400px" perrow="6">
Image:64 58 Affine 200 Iterations.JPG
Image:64 58 Affine 300 Iterations.JPG
Image:64 58 Affine 400 Iterations.JPG
</gallery>

==='''Update May 14, 2009'''===
We tried '''Affine registration''' for the above mentioned patients with 64 as fixed MRI and 58 as moving MRI and '''10''' landmark point. The zipped file which contains scene along with screenshots is
[[Image:Affine Registration 10 Landmark Points Varying Iterations.zip |left|64_Affine_Registered_10_Landmarks_Various_Iterations_Scene]]. We varied the number of iterations from 50 to 450.
<gallery widths="200px" perrow="6">
Image:64 58 Affine 50 Iterations 10 Landmark Points.jpg
Image:64 58 Affine 100 Iterations 10 Landmark Points.jpg
Image:64 58 Affine 150 Iterations 10 Landmark Points.jpg
Image:64 58 Affine 250 Iterations 10 Landmark Points.jpg
Image:64 58 Affine 350 Iterations 10 Landmark Points.jpg
Image:64 58 Affine 450 Iterations 10 Landmark Points.jpg
</gallery>

==='''Update May 20, 2009'''===
We tried '''BSpline registration''' for the above mentioned patients with 64 as fixed MRI and 58 as moving MRI and '''no''' landmark point. The zipped file which contains scene along with screenshots is
[[Image:Affine Registration 10 Landmark Points Varying Iterations.zip |left|64_Affine_Registered_10_Landmarks_Various_Iterations_Scene]]. We varied the number of iterations from 50 to 450.
<gallery widths="200px" perrow="6">
Image:64 58 BSpline 10 Iterations.JPG
Image:64 58 BSpline 110 Iterations.JPG
Image:64 58 BSpline 160 Iterations.JPG
Image:64 58 BSpline 210 Iterations.JPG
Image:64 58 BSpline 260 Iterations.JPG
Image:64 58 BSpline 360 Iterations.JPG
</gallery>

==='''Update May 21, 2009'''===
We tried '''Pipelined Affine registration''' for the above mentioned patients with 64 as fixed MRI and 58 as moving MRI and '''no''' landmark point. The zipped file which contains scene along with screenshots is
[[Image:Affine Registration 10 Landmark Points Varying Iterations.zip |left|64_Affine_Registered_10_Landmarks_Various_Iterations_Scene]]. We varied the number of iterations from 50 to 450.
<gallery widths="200px" perrow="6">
Image:64 58 PipeLinedAffine Registration 50 Iterations.JPG
Image:64 58 PipeLinedAffine Registration 100 Iterations.JPG
Image:64 58 PipeLinedAffine Registration 150 Iterations.JPG
Image:64 58 PipeLinedAffine Registration 200 Iterations.JPG
Image:64 58 PipeLinedAffine Registration 300 Iterations.JPG
Image:64 58 PipeLinedAffine Registration 400 Iterations.JPG
</gallery>

===Multi Image Registration===
We have also tried the approach of Non-rigid Groupwise Registration using Bspline Deformation Model by Serdar K. Balci, Polina Golland and William M. Wells. In this approach, we try to give one slice each of two different patients we need to register as input. Here are some of the results of their approach.

<gallery widths="200px" perrow="2">
Slice 1:
Image:1_Slicez0.JPG‎
Slice 2:
Image:1_Slicez1.JPG
Mean Slice of above two :
Image:1_MeanSliceZ.JPG
</gallery>

<gallery widths="200px" perrow="2">
Slice 1:
Image:2_Z0.JPG
Slice 2:
Image:2_Z1.JPG
Mean Slice of above two :
Image:2_MeanSliceZ.JPG
</gallery>

File:64 58 PipeLinedAffine Registration 400 Iterations.JPG

2009-05-20T19:40:47Z

Harishd:

File:64 58 PipeLinedAffine Registration 300 Iterations.JPG

2009-05-20T19:39:43Z

Harishd:

File:64 58 PipeLinedAffine Registration 200 Iterations.JPG

2009-05-20T19:39:31Z

Harishd:

File:64 58 PipeLinedAffine Registration 150 Iterations.JPG

2009-05-20T19:25:47Z

Harishd:

File:64 58 PipeLinedAffine Registration 100 Iterations.JPG

2009-05-20T19:24:05Z

Harishd:

File:64 58 PipeLinedAffine Registration 50 Iterations.JPG

2009-05-20T19:23:51Z

Harishd:

Stanford Simbios group

2009-05-20T19:07:14Z

Harishd:

<gallery widths="400px" perrow="6">
Image:58.jpg
Image:All_Three.JPG
Image:Femur_Patella_Tibia.jpg
</gallery>

=Aim=
To develop a generic semi-automatic segmentation toolkit to convert images of musculoskeletal structures to 3D models.

=Process Flowchart=
[[Image:FlowChart.jpg|left| Process Diagram.]]

=Progress=
==Atlas Generation from Input MR Images==
===Model Generation from Input MR Images===
Pre-Segmented models for femur, patella and tibia are obtained for a patient (in .stl format).
[[Image:All_Three.JPG|left|thumb| Pre-Segmented Femur/Patella/Tibia Model]]

===Create a filled label map using PolyDataToFilledLabelMap module in slicer===
The models generated in the above step are closed but hollow. But EM Segmentation requires the atlas given to be in closed filled format. Hence we converted the hollow closed models into filled label volumes using the module PolyDataToFilledLabelMap module in Slicer. The output label map of this module is of datatype unsigned char and is converted to short datatype.
<gallery widths="400px" perrow="6">
Image:LabelMap_Femur.jpg
Image:LabelMap_Tibia.jpg
Image:LabelMap_Patella.jpg
</gallery>

===EM Segmentation based on the atlas===
The output label map in the above is given as input atlas in the EM Segmentation step. As an initial step we ran EM Segmentation on the same patient. The EM Segmented output is given in the below figure.
[[Image:Femur_Patella_Tibia.jpg|left|thumb| EM Segmented Output]]
==Register Images==
===Register Images Module in Slicer===
We are now in the process of trying to register new patient image to the existing patient image. If we register the images, we can get the transform which can be used to register the existing atlas (label map) to the new patient.

We think that Pipelined Bspline Registration may give promising results for our dataset. Below are some of the results. Here we consider 64_MRI as the fixed image and 58_MRI as the moving image and used '''Pipelined BSpline Image registration method''' for registration. Also we gave one point as landmark point.
<gallery widths="400px" perrow="6">
Image:58_MRI.jpg
Image:64_MRI.jpg
Image:64_Registered.jpg
</gallery>

We also tried '''affine registration''' for the above mentioned patients with 64 as fixed MRI and 58 as moving MRI and '''one landmark point'''. The zipped file which contains scene along with screenshots is
[[Image:64 58 Affine Registration.zip |left|64_Affine_Registered_Scene]]
<gallery widths="400px" perrow="6">
Image:64 Affine Registration.JPG
Image:64 Registerd SuperImposed On 64 Actual.JPG
</gallery>

We also tried '''BSpline registration''' for the above mentioned patients with 64 as fixed MRI and 58 as moving MRI and one landmark point. The zipped file which contains scene along with screenshots is
[[Image:64 58 BSpline Registration.zip |left|64_BSpline_Registered_Scene]]. Here we used '''4 points''' as landmarks.
<gallery widths="400px" perrow="6">
Image:64 BSpline Registration.JPG
Image:64 BSpline Registered SuperImposed On 64 Actual.JPG
</gallery>

We also tried '''Affine registration''' for the above mentioned patients with 64 as fixed MRI and 58 as moving MRI and four landmark point. The zipped file which contains scene along with screenshots is
[[Image:64_58_Affine_Various_Iterations.zip |left|64_Affine_Registered_Various_Iterations_Scene]]. Here we used '''4 points''' as landmarks. We varied the number of iterations from 200 to 400.
<gallery widths="400px" perrow="6">
Image:64 58 Affine 200 Iterations.JPG
Image:64 58 Affine 300 Iterations.JPG
Image:64 58 Affine 400 Iterations.JPG
</gallery>

==='''Update May 13, 2009'''===
We tried '''Affine registration''' for the above mentioned patients with 64 as fixed MRI and 58 as moving MRI and '''no''' landmark point. The zipped file which contains scene along with screenshots is
[[Image:64_58_Affine_Various_Iterations.zip |left|64_Affine_Registered_Various_Iterations_Scene]]. We varied the number of iterations from 200 to 400.
<gallery widths="400px" perrow="6">
Image:64 58 Affine 200 Iterations.JPG
Image:64 58 Affine 300 Iterations.JPG
Image:64 58 Affine 400 Iterations.JPG
</gallery>

==='''Update May 14, 2009'''===
We tried '''Affine registration''' for the above mentioned patients with 64 as fixed MRI and 58 as moving MRI and '''10''' landmark point. The zipped file which contains scene along with screenshots is
[[Image:Affine Registration 10 Landmark Points Varying Iterations.zip |left|64_Affine_Registered_10_Landmarks_Various_Iterations_Scene]]. We varied the number of iterations from 50 to 450.
<gallery widths="200px" perrow="6">
Image:64 58 Affine 50 Iterations 10 Landmark Points.jpg
Image:64 58 Affine 100 Iterations 10 Landmark Points.jpg
Image:64 58 Affine 150 Iterations 10 Landmark Points.jpg
Image:64 58 Affine 250 Iterations 10 Landmark Points.jpg
Image:64 58 Affine 350 Iterations 10 Landmark Points.jpg
Image:64 58 Affine 450 Iterations 10 Landmark Points.jpg
</gallery>

==='''Update May 20, 2009'''===
We tried '''BSpline registration''' for the above mentioned patients with 64 as fixed MRI and 58 as moving MRI and '''no''' landmark point. The zipped file which contains scene along with screenshots is
[[Image:Affine Registration 10 Landmark Points Varying Iterations.zip |left|64_Affine_Registered_10_Landmarks_Various_Iterations_Scene]]. We varied the number of iterations from 50 to 450.
<gallery widths="200px" perrow="6">
Image:64 58 BSpline 10 Iterations.JPG
Image:64 58 BSpline 110 Iterations.JPG
Image:64 58 BSpline 160 Iterations.JPG
Image:64 58 BSpline 210 Iterations.JPG
Image:64 58 BSpline 260 Iterations.JPG
Image:64 58 BSpline 360 Iterations.JPG
</gallery>

===Multi Image Registration===
We have also tried the approach of Non-rigid Groupwise Registration using Bspline Deformation Model by Serdar K. Balci, Polina Golland and William M. Wells. In this approach, we try to give one slice each of two different patients we need to register as input. Here are some of the results of their approach.

<gallery widths="200px" perrow="2">
Slice 1:
Image:1_Slicez0.JPG‎
Slice 2:
Image:1_Slicez1.JPG
Mean Slice of above two :
Image:1_MeanSliceZ.JPG
</gallery>

<gallery widths="200px" perrow="2">
Slice 1:
Image:2_Z0.JPG
Slice 2:
Image:2_Z1.JPG
Mean Slice of above two :
Image:2_MeanSliceZ.JPG
</gallery>

File:64 58 BSpline 360 Iterations.JPG

2009-05-20T19:06:20Z

Harishd:

File:64 58 BSpline 260 Iterations.JPG

2009-05-20T19:06:07Z

Harishd:

File:64 58 BSpline 210 Iterations.JPG

2009-05-20T19:05:54Z

Harishd:

File:64 58 BSpline 160 Iterations.JPG

2009-05-20T19:05:41Z

Harishd:

File:64 58 BSpline 110 Iterations.JPG

2009-05-20T19:05:28Z

Harishd:

File:64 58 BSpline 10 Iterations.JPG

2009-05-20T19:02:51Z

Harishd:

Stanford Simbios group

2009-05-14T22:53:12Z

Harishd:

<gallery widths="400px" perrow="6">
Image:58.jpg
Image:All_Three.JPG
Image:Femur_Patella_Tibia.jpg
</gallery>

=Aim=
To develop a generic semi-automatic segmentation toolkit to convert images of musculoskeletal structures to 3D models.

=Process Flowchart=
[[Image:FlowChart.jpg|left| Process Diagram.]]

=Progress=
==Atlas Generation from Input MR Images==
===Model Generation from Input MR Images===
Pre-Segmented models for femur, patella and tibia are obtained for a patient (in .stl format).
[[Image:All_Three.JPG|left|thumb| Pre-Segmented Femur/Patella/Tibia Model]]

===Create a filled label map using PolyDataToFilledLabelMap module in slicer===
The models generated in the above step are closed but hollow. But EM Segmentation requires the atlas given to be in closed filled format. Hence we converted the hollow closed models into filled label volumes using the module PolyDataToFilledLabelMap module in Slicer. The output label map of this module is of datatype unsigned char and is converted to short datatype.
<gallery widths="400px" perrow="6">
Image:LabelMap_Femur.jpg
Image:LabelMap_Tibia.jpg
Image:LabelMap_Patella.jpg
</gallery>

===EM Segmentation based on the atlas===
The output label map in the above is given as input atlas in the EM Segmentation step. As an initial step we ran EM Segmentation on the same patient. The EM Segmented output is given in the below figure.
[[Image:Femur_Patella_Tibia.jpg|left|thumb| EM Segmented Output]]
==Register Images==
===Register Images Module in Slicer===
We are now in the process of trying to register new patient image to the existing patient image. If we register the images, we can get the transform which can be used to register the existing atlas (label map) to the new patient.

We think that Pipelined Bspline Registration may give promising results for our dataset. Below are some of the results. Here we consider 64_MRI as the fixed image and 58_MRI as the moving image and used '''Pipelined BSpline Image registration method''' for registration. Also we gave one point as landmark point.
<gallery widths="400px" perrow="6">
Image:58_MRI.jpg
Image:64_MRI.jpg
Image:64_Registered.jpg
</gallery>

We also tried '''affine registration''' for the above mentioned patients with 64 as fixed MRI and 58 as moving MRI and '''one landmark point'''. The zipped file which contains scene along with screenshots is
[[Image:64 58 Affine Registration.zip |left|64_Affine_Registered_Scene]]
<gallery widths="400px" perrow="6">
Image:64 Affine Registration.JPG
Image:64 Registerd SuperImposed On 64 Actual.JPG
</gallery>

We also tried '''BSpline registration''' for the above mentioned patients with 64 as fixed MRI and 58 as moving MRI and one landmark point. The zipped file which contains scene along with screenshots is
[[Image:64 58 BSpline Registration.zip |left|64_BSpline_Registered_Scene]]. Here we used '''4 points''' as landmarks.
<gallery widths="400px" perrow="6">
Image:64 BSpline Registration.JPG
Image:64 BSpline Registered SuperImposed On 64 Actual.JPG
</gallery>

We also tried '''Affine registration''' for the above mentioned patients with 64 as fixed MRI and 58 as moving MRI and four landmark point. The zipped file which contains scene along with screenshots is
[[Image:64_58_Affine_Various_Iterations.zip |left|64_Affine_Registered_Various_Iterations_Scene]]. Here we used '''4 points''' as landmarks. We varied the number of iterations from 200 to 400.
<gallery widths="400px" perrow="6">
Image:64 58 Affine 200 Iterations.JPG
Image:64 58 Affine 300 Iterations.JPG
Image:64 58 Affine 400 Iterations.JPG
</gallery>

==='''Update May 13, 2009'''===
We tried '''Affine registration''' for the above mentioned patients with 64 as fixed MRI and 58 as moving MRI and '''no''' landmark point. The zipped file which contains scene along with screenshots is
[[Image:64_58_Affine_Various_Iterations.zip |left|64_Affine_Registered_Various_Iterations_Scene]]. We varied the number of iterations from 200 to 400.
<gallery widths="400px" perrow="6">
Image:64 58 Affine 200 Iterations.JPG
Image:64 58 Affine 300 Iterations.JPG
Image:64 58 Affine 400 Iterations.JPG
</gallery>

==='''Update May 14, 2009'''===
We tried '''Affine registration''' for the above mentioned patients with 64 as fixed MRI and 58 as moving MRI and '''10''' landmark point. The zipped file which contains scene along with screenshots is
[[Image:Affine Registration 10 Landmark Points Varying Iterations.zip |left|64_Affine_Registered_10_Landmarks_Various_Iterations_Scene]]. We varied the number of iterations from 50 to 450.
<gallery widths="200px" perrow="6">
Image:64 58 Affine 50 Iterations 10 Landmark Points.jpg
Image:64 58 Affine 100 Iterations 10 Landmark Points.jpg
Image:64 58 Affine 150 Iterations 10 Landmark Points.jpg
Image:64 58 Affine 250 Iterations 10 Landmark Points.jpg
Image:64 58 Affine 350 Iterations 10 Landmark Points.jpg
Image:64 58 Affine 450 Iterations 10 Landmark Points.jpg
</gallery>

===Multi Image Registration===
We have also tried the approach of Non-rigid Groupwise Registration using Bspline Deformation Model by Serdar K. Balci, Polina Golland and William M. Wells. In this approach, we try to give one slice each of two different patients we need to register as input. Here are some of the results of their approach.

<gallery widths="200px" perrow="2">
Slice 1:
Image:1_Slicez0.JPG‎
Slice 2:
Image:1_Slicez1.JPG
Mean Slice of above two :
Image:1_MeanSliceZ.JPG
</gallery>

<gallery widths="200px" perrow="2">
Slice 1:
Image:2_Z0.JPG
Slice 2:
Image:2_Z1.JPG
Mean Slice of above two :
Image:2_MeanSliceZ.JPG
</gallery>

File:Affine Registration 10 Landmark Points Varying Iterations.zip

2009-05-14T22:52:24Z

Harishd:

Stanford Simbios group

2009-05-14T21:58:30Z

Harishd:

<gallery widths="400px" perrow="6">
Image:58.jpg
Image:All_Three.JPG
Image:Femur_Patella_Tibia.jpg
</gallery>

=Aim=
To develop a generic semi-automatic segmentation toolkit to convert images of musculoskeletal structures to 3D models.

=Process Flowchart=
[[Image:FlowChart.jpg|left| Process Diagram.]]

=Progress=
==Atlas Generation from Input MR Images==
===Model Generation from Input MR Images===
Pre-Segmented models for femur, patella and tibia are obtained for a patient (in .stl format).
[[Image:All_Three.JPG|left|thumb| Pre-Segmented Femur/Patella/Tibia Model]]

===Create a filled label map using PolyDataToFilledLabelMap module in slicer===
The models generated in the above step are closed but hollow. But EM Segmentation requires the atlas given to be in closed filled format. Hence we converted the hollow closed models into filled label volumes using the module PolyDataToFilledLabelMap module in Slicer. The output label map of this module is of datatype unsigned char and is converted to short datatype.
<gallery widths="400px" perrow="6">
Image:LabelMap_Femur.jpg
Image:LabelMap_Tibia.jpg
Image:LabelMap_Patella.jpg
</gallery>

===EM Segmentation based on the atlas===
The output label map in the above is given as input atlas in the EM Segmentation step. As an initial step we ran EM Segmentation on the same patient. The EM Segmented output is given in the below figure.
[[Image:Femur_Patella_Tibia.jpg|left|thumb| EM Segmented Output]]
==Register Images==
===Register Images Module in Slicer===
We are now in the process of trying to register new patient image to the existing patient image. If we register the images, we can get the transform which can be used to register the existing atlas (label map) to the new patient.

We think that Pipelined Bspline Registration may give promising results for our dataset. Below are some of the results. Here we consider 64_MRI as the fixed image and 58_MRI as the moving image and used '''Pipelined BSpline Image registration method''' for registration. Also we gave one point as landmark point.
<gallery widths="400px" perrow="6">
Image:58_MRI.jpg
Image:64_MRI.jpg
Image:64_Registered.jpg
</gallery>

We also tried '''affine registration''' for the above mentioned patients with 64 as fixed MRI and 58 as moving MRI and '''one landmark point'''. The zipped file which contains scene along with screenshots is
[[Image:64 58 Affine Registration.zip |left|64_Affine_Registered_Scene]]
<gallery widths="400px" perrow="6">
Image:64 Affine Registration.JPG
Image:64 Registerd SuperImposed On 64 Actual.JPG
</gallery>

We also tried '''BSpline registration''' for the above mentioned patients with 64 as fixed MRI and 58 as moving MRI and one landmark point. The zipped file which contains scene along with screenshots is
[[Image:64 58 BSpline Registration.zip |left|64_BSpline_Registered_Scene]]. Here we used '''4 points''' as landmarks.
<gallery widths="400px" perrow="6">
Image:64 BSpline Registration.JPG
Image:64 BSpline Registered SuperImposed On 64 Actual.JPG
</gallery>

We also tried '''Affine registration''' for the above mentioned patients with 64 as fixed MRI and 58 as moving MRI and four landmark point. The zipped file which contains scene along with screenshots is
[[Image:64_58_Affine_Various_Iterations.zip |left|64_Affine_Registered_Various_Iterations_Scene]]. Here we used '''4 points''' as landmarks. We varied the number of iterations from 200 to 400.
<gallery widths="400px" perrow="6">
Image:64 58 Affine 200 Iterations.JPG
Image:64 58 Affine 300 Iterations.JPG
Image:64 58 Affine 400 Iterations.JPG
</gallery>

==='''Update May 13, 2009'''===
We tried '''Affine registration''' for the above mentioned patients with 64 as fixed MRI and 58 as moving MRI and '''no''' landmark point. The zipped file which contains scene along with screenshots is
[[Image:64_58_Affine_Various_Iterations.zip |left|64_Affine_Registered_Various_Iterations_Scene]]. We varied the number of iterations from 200 to 400.
<gallery widths="400px" perrow="6">
Image:64 58 Affine 200 Iterations.JPG
Image:64 58 Affine 300 Iterations.JPG
Image:64 58 Affine 400 Iterations.JPG
</gallery>

==='''Update May 14, 2009'''===
We tried '''Affine registration''' for the above mentioned patients with 64 as fixed MRI and 58 as moving MRI and '''10''' landmark point. The zipped file which contains scene along with screenshots is
[[Image:64_58_Affine_Various_Iterations.zip |left|64_Affine_Registered_Various_Iterations_Scene]]. We varied the number of iterations from 50 to 450.
<gallery widths="200px" perrow="6">
Image:64 58 Affine 50 Iterations 10 Landmark Points.jpg
Image:64 58 Affine 100 Iterations 10 Landmark Points.jpg
Image:64 58 Affine 150 Iterations 10 Landmark Points.jpg
Image:64 58 Affine 250 Iterations 10 Landmark Points.jpg
Image:64 58 Affine 350 Iterations 10 Landmark Points.jpg
Image:64 58 Affine 450 Iterations 10 Landmark Points.jpg
</gallery>

===Multi Image Registration===
We have also tried the approach of Non-rigid Groupwise Registration using Bspline Deformation Model by Serdar K. Balci, Polina Golland and William M. Wells. In this approach, we try to give one slice each of two different patients we need to register as input. Here are some of the results of their approach.

<gallery widths="200px" perrow="2">
Slice 1:
Image:1_Slicez0.JPG‎
Slice 2:
Image:1_Slicez1.JPG
Mean Slice of above two :
Image:1_MeanSliceZ.JPG
</gallery>

<gallery widths="200px" perrow="2">
Slice 1:
Image:2_Z0.JPG
Slice 2:
Image:2_Z1.JPG
Mean Slice of above two :
Image:2_MeanSliceZ.JPG
</gallery>

File:64 58 Affine 450 Iterations 10 Landmark Points.jpg

2009-05-14T21:57:19Z

Harishd:

File:64 58 Affine 350 Iterations 10 Landmark Points.jpg

2009-05-14T21:57:04Z

Harishd:

File:64 58 Affine 250 Iterations 10 Landmark Points.jpg

2009-05-14T21:56:48Z

Harishd:

File:64 58 Affine 150 Iterations 10 Landmark Points.jpg

2009-05-14T21:56:32Z

Harishd:

File:64 58 Affine 100 Iterations 10 Landmark Points.jpg

2009-05-14T21:56:14Z

Harishd:

File:64 58 Affine 50 Iterations 10 Landmark Points.jpg

2009-05-14T21:54:13Z

Harishd:

Stanford Simbios group

2009-05-14T00:22:06Z

Harishd:

File:Affine Registration Results Varying Iterations Scene File.zip

2009-05-14T00:21:28Z

Harishd:

Stanford Simbios group

2009-05-14T00:16:33Z

Harishd:

Stanford Simbios group

2009-05-14T00:15:05Z

Harishd:

File:64 58 Affine 500 Iterations.jpeg

2009-05-14T00:12:54Z

Harishd:

File:64 58 Affine 450 Iterations.jpeg

2009-05-14T00:12:12Z

Harishd:

File:64 58 Affine 350 Iterations.jpeg

2009-05-14T00:11:45Z

Harishd:

File:64 58 Affine 250 Iterations.jpeg

2009-05-14T00:11:16Z

Harishd:

File:64 58 Affine 150 Iterations.jpeg

2009-05-14T00:09:40Z

Harishd:

Stanford Simbios group

2009-05-12T21:34:02Z

Harishd:

File:64 58 Affine Various Iterations.zip

2009-05-12T21:32:43Z

Harishd:

Stanford Simbios group

2009-05-12T21:18:17Z

Harishd:

File:64 58 Affine 400 Iterations.JPG

2009-05-12T21:15:20Z

Harishd:

File:64 58 Affine 300 Iterations.JPG

2009-05-12T21:14:19Z

Harishd:

File:64 58 Affine 200 Iterations.JPG

2009-05-12T21:13:06Z

Harishd: 64 Affine Registered Super imposed on 64 Actual

64 Affine Registered Super imposed on 64 Actual

Stanford Simbios group

2009-05-08T19:02:59Z

Harishd:

File:64 BSpline Registered SuperImposed On 64 Actual.JPG

2009-05-08T18:57:36Z

Harishd: Superimposition of 64 bspline registered on 64 actual MRI image.

Superimposition of 64 bspline registered on 64 actual MRI image.

File:64 Registerd SuperImposed On 64 Actual.JPG

2009-05-08T17:24:40Z

Harishd: This image tells us how much match we are getting by superimposing on actual mri image...

This image tells us how much match we are getting by superimposing on actual mri image...

Stanford Simbios group

2009-05-08T17:15:12Z

Harishd:

File:64 BSpline Registration.JPG

2009-05-08T17:14:30Z

Harishd:

File:64 58 BSpline Registration.zip

2009-05-08T17:12:18Z

Harishd: This file contains the screenshots as well as the scene files of performing BSpline registration of 64 as fixed and 58 as moving image.

This file contains the screenshots as well as the scene files of performing BSpline registration of 64 as fixed and 58 as moving image.