Difference between revisions of "Stanford Simbios group"

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==Dataset for Patient 58==
==Dataset for Patient 58==
[[Image:58_IM-0009-0001.zip|left|Patient 58]]
[[Image:58_IM-0009-0001.zip|left|Patient 58]]
==Dataset for Patient 65==
==Dataset for Patient 57==
[[Image:57_IM-0009-0001.zip|left|Patient 57]]
[[Image:57_IM-0009-0001.zip|left|Patient 57]]
==Dataset for Patient 65==
==Dataset for Patient 65==
[[Image:65_IM-0009-0001.zip|left|Patient 65]]
[[Image:65_IM-0009-0001.zip|left|Patient 65]]

Revision as of 23:01, 29 April 2009

Home < Stanford Simbios group


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

Process Flowchart

Process Diagram.


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). This models can be prepared using the hypermesh software. The models contain vertices of triangles representing femur, tibia and patella regions.

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.

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.

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.


Below are the datasets that we used in our experiments. All the images are in standard dicom format.

Dataset for Patient 58

File:58 IM-0009-0001.zip

Dataset for Patient 57

File:57 IM-0009-0001.zip

Dataset for Patient 65

File:65 IM-0009-0001.zip