Main Page/SlicerCommunity/2019 - Revision history

Marianna: /* The Middle Fossa Approach with Self-drilling Screws: a Novel Technique for BONEBRIDGE Implantation */

2020-02-12T20:12:17Z

The Middle Fossa Approach with Self-drilling Screws: a Novel Technique for BONEBRIDGE Implantation

Marianna: /* Semi-Automatic Signature-Based Segmentation Method for Quantification of Neuromelanin in Substantia Nigra */

2020-02-12T20:11:39Z

Semi-Automatic Signature-Based Segmentation Method for Quantification of Neuromelanin in Substantia Nigra

Marianna at 20:04, 12 February 2020

2020-02-12T20:04:22Z

Show changes

Marianna: /* Semi-Automatic Signature-Based Segmentation Method for Quantification of Neuromelanin in Substantia Nigra */

2020-02-12T19:52:36Z

Semi-Automatic Signature-Based Segmentation Method for Quantification of Neuromelanin in Substantia Nigra

Marianna: /* Morphological Analysis of Sigmoid Sinus Anatomy: Clinical Applications to Neurotological Surgery */

2020-02-12T19:47:59Z

Morphological Analysis of Sigmoid Sinus Anatomy: Clinical Applications to Neurotological Surgery

Marianna: /* Morphological Analysis of Sigmoid Sinus Anatomy: Clinical Applications to Neurotological Surgery */

2020-02-12T19:43:32Z

Morphological Analysis of Sigmoid Sinus Anatomy: Clinical Applications to Neurotological Surgery

Marianna: /* Morphological Analysis of Sigmoid Sinus Anatomy: Clinical Applications to Neurotological Surgery */

2020-02-12T19:38:34Z

Morphological Analysis of Sigmoid Sinus Anatomy: Clinical Applications to Neurotological Surgery

Marianna: /* Morphological Analysis of Sigmoid Sinus Anatomy: Clinical Applications to Neurotological Surgery */

2020-02-12T19:34:19Z

Morphological Analysis of Sigmoid Sinus Anatomy: Clinical Applications to Neurotological Surgery

Marianna: /* Investigation of F-BAR Domain PACSIN Proteins Uncovers Membrane Tubulation Function in Cilia Assembly and Transport */

2020-02-12T19:31:59Z

Investigation of F-BAR Domain PACSIN Proteins Uncovers Membrane Tubulation Function in Cilia Assembly and Transport

Marianna: /* Reproducibility And Non-Redundancy Of Radiomic Features Extracted From Arterial Phase CT Scans In Hepatocellular Carcinoma Patients: Impact Of Tumor Segmentation Variability */

2020-02-12T19:28:36Z

Reproducibility And Non-Redundancy Of Radiomic Features Extracted From Arterial Phase CT Scans In Hepatocellular Carcinoma Patients: Impact Of Tumor Segmentation Variability

@@ Line 835: / Line 835: @@
 It is possible to use MRI to identify the volume of interest with the maximum anatomic cross section of the PVM belly while minimizing the inadvertent inclusion of adjacent PRM or ICM in that measurement.
 |}
 ==A Comparative Evaluation of SteamVR Tracking and the OptiTrack System for Medical Device Tracking==

@@ Line 2,063: / Line 2,063: @@
 |}
-==Semi-Automatic Signature-Based Segmentation Method for Quantification of Neuromelanin in Substantia Nigra==
+==Imaging as Part of a Quality Assurance Program: Predictors of Interobserver Variability for Pretreatment Image Registration for Lung SBRT==
 {| width="100%"

← Older revision		Revision as of 19:52, 12 February 2020
Line 2,027:		Line 2,027:

	\| align="right" \|[[image:vanOschJoHNS2019.jpg\|thumb\|300px\| Fiducials and distances calculated from coordinates (BLUE - SS, YELLOW - FN, PURPLE - EAC). a. EAC – SS (F1 – F2); Descending FN – SS (F3 – F4). b. 2nd genu FN – Superior tip JB. (F5 – F6); Descending FN - Superior tip JB (F6 – F7); FN at the SMF - JB (F8 – F9).]]		\| align="right" \|[[image:vanOschJoHNS2019.jpg\|thumb\|300px\| Fiducials and distances calculated from coordinates (BLUE - SS, YELLOW - FN, PURPLE - EAC). a. EAC – SS (F1 – F2); Descending FN – SS (F3 – F4). b. 2nd genu FN – Superior tip JB. (F5 – F6); Descending FN - Superior tip JB (F6 – F7); FN at the SMF - JB (F8 – F9).]]
		+	\|}
		+
		+	==Biomarker Localization, Analysis, Visualization, Extraction, and Registration (BLAzER) Methodology for Research and Clinical Brain PET Applications==
		+
		+	{\| width="100%"
		+	\|
		+	'''Publication:''' [https://www.ncbi.nlm.nih.gov/pubmed/31322571 J Alzheimers Dis. 2019;70(4):1241-57. PMID: 31322571] \| [https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6745194/pdf/nihms-1048057.pdf PDF]
		+
		+	'''Authors:''' Raman F, Grandhi S, Murchison CF, Kennedy RE, Landau S, Roberson ED, McConathy J; Alzheimer’s Disease Neuroimaging Initiative.
		+
		+	'''Institution:''' Department of Radiology, University of Alabama at Birmingham, Birmingham, AL, USA.
		+
		+	'''Abstract:''' BACKGROUND:
		+	Tools for efficient evaluation of amyloid- and tau-PET images are needed in both clinical and research settings.
		+
		+	OBJECTIVE:
		+	This study was designed to validate a semi-automated image analysis methodology, called Biomarker Localization, Analysis, Visualization, Extraction, and Registration (BLAzER). We tested BLAzER using two different segmentation platforms, FreeSurfer (FS) and Neuroreader (NR), for regional brain PET quantification in participants in the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset.
		+
		+	METHODS:
		+	127 amyloid-PET and 55 tau-PET studies with volumetric MRIs were obtained from ADNI. The BLAzER methodology utilizes segmentation of MR images by FS or NR, then visualizes and quantifies regional brain PET data using FDA-cleared software (MIM), enabling quality control to ensure optimal registration and to detect segmentation errors.
		+
		+	RESULTS:
		+	BLAzER analysis required ∼5 min plus segmentation time. BLAzER using FS segmentation showed strong agreement with ADNI for global amyloid-PET standardized uptake value ratios (SUVRs) (r = 0.9922, p < 0.001) and regional tau-PET SUVRs across all Braak staging regions (r > 0.97, p < 0.001) with high inter-operator reproducibility (ICC > 0.97) and nearly identical dichotomization as amyloid-positive or -negative (2 discrepant cases out of 127). Comparing FS versus NR segmentation with BLAzER, global SUVRs were strongly correlated for amyloid-PET (r = 0.9841, p < 0.001), but were systematically higher (4% on average) with NR, likely due to more inclusion of white matter with NR-defined regions.
		+
		+	CONCLUSIONS:
		+	BLAzER provides an efficient methodology for regional brain PET quantification. FDA-cleared components and visualization of registration reduce barriers between research and clinical applications.
		+
		+	"...Upon completion of segmentation, parcellated, segmented brain images were converted from MGZ to DICOM format using [http://www.slicer.org '''3D Slicer'''] v4.5.0."
		+
		+	'''Funding:'''
		+
		+	*RF1 AG059009/AG/NIA NIH HHS/United States
		+	*T32 GM008361/GM/NIGMS NIH HHS/United States
		+	*U01 AG024904/AG/NIA NIH HHS/United States
	\|}		\|}

← Older revision		Revision as of 19:47, 12 February 2020
Line 1,971:		Line 1,971:

	"...used [http://www.slicer.org '''3D Slicer'''] to generate a triangular surface model of the prostate from the contours of the organ and the urethra drawn on the T2-weighted MR images, using the provided prostate segmentation as guidance."		"...used [http://www.slicer.org '''3D Slicer'''] to generate a triangular surface model of the prostate from the contours of the organ and the urethra drawn on the T2-weighted MR images, using the provided prostate segmentation as guidance."
		+	\|}
		+
		+	==Machine Learning Derived Segmentation of Phase Velocity Encoded Cardiovascular Magnetic Resonance for Fully Automated Aortic Flow Quantification==
		+
		+	{\| width="100%"
		+	\|
		+	'''Publication:''' [https://www.ncbi.nlm.nih.gov/pubmed/30612574 J Cardiovasc Magn Reson. 2019 Jan 7;21(1):1. PMID: 30612574] \| [https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6322266/pdf/12968_2018_Article_509.pdf PDF]
		+
		+	'''Authors:''' Bratt A, Kim J, Pollie M, Beecy AN, Tehrani NH, Codella N, Perez-Johnston R, Palumbo MC, Alakbarli J, Colizza W, Drexler IR, Azevedo CF, Kim RJ, Devereux RB, Weinsaft JW.
		+
		+	'''Institution:''' Department of Radiology, Weill Cornell Medicine, New York, NY, USA.
		+
		+	'''Abstract:''' BACKGROUND:
		+	Phase contrast (PC) cardiovascular magnetic resonance (CMR) is widely employed for flow quantification, but analysis typically requires time consuming manual segmentation which can require human correction. Advances in machine learning have markedly improved automated processing, but have yet to be applied to PC-CMR. This study tested a novel machine learning model for fully automated analysis of PC-CMR aortic flow.
		+
		+	METHODS:
		+	A machine learning model was designed to track aortic valve borders based on neural network approaches. The model was trained in a derivation cohort encompassing 150 patients who underwent clinical PC-CMR then compared to manual and commercially-available automated segmentation in a prospective validation cohort. Further validation testing was performed in an external cohort acquired from a different site/CMR vendor.
		+
		+	RESULTS:
		+	Among 190 coronary artery disease patients prospectively undergoing CMR on commercial scanners (84% 1.5T, 16% 3T), machine learning segmentation was uniformly successful, requiring no human intervention: Segmentation time was < 0.01 min/case (1.2 min for entire dataset); manual segmentation required 3.96 ± 0.36 min/case (12.5 h for entire dataset). Correlations between machine learning and manual segmentation-derived flow approached unity (r = 0.99, p < 0.001). Machine learning yielded smaller absolute differences with manual segmentation than did commercial automation (1.85 ± 1.80 vs. 3.33 ± 3.18 mL, p < 0.01): Nearly all (98%) of cases differed by ≤5 mL between machine learning and manual methods. Among patients without advanced mitral regurgitation, machine learning correlated well (r = 0.63, p < 0.001) and yielded small differences with cine-CMR stroke volume (∆ 1.3 ± 17.7 mL, p = 0.36). Among advanced mitral regurgitation patients, machine learning yielded lower stroke volume than did volumetric cine-CMR (∆ 12.6 ± 20.9 mL, p = 0.005), further supporting validity of this method. Among the external validation cohort (n = 80) acquired using a different CMR vendor, the algorithm yielded equivalently small differences (∆ 1.39 ± 1.77 mL, p = 0.4) and high correlations (r = 0.99, p < 0.001) with manual segmentation, including similar results in 20 patients with bicuspid or stenotic aortic valve pathology (∆ 1.71 ± 2.25 mL, p = 0.25).
		+
		+	CONCLUSION:
		+	Fully automated machine learning PC-CMR segmentation performs robustly for aortic flow quantification - yielding rapid segmentation, small differences with manual segmentation, and identification of differential forward/left ventricular volumetric stroke volume in context of concomitant mitral regurgitation. Findings support use of machine learning for analysis of large scale CMR datasets.
		+
		+	"...labeling pixels in the magnitude images as either valve or non-valve using [http://www.slicer.org '''3D Slicer'''], an open-source medical image post-processing application."
	\|}		\|}

← Older revision		Revision as of 19:43, 12 February 2020
Line 1,956:		Line 1,956:

	"...For the nine patients that had pre- and post- treatment scans, the total lateral and third ventricle volumes were calculated ([http://www.slicer.org '''3D Slicer''']). "		"...For the nine patients that had pre- and post- treatment scans, the total lateral and third ventricle volumes were calculated ([http://www.slicer.org '''3D Slicer''']). "
		+	\|}
		+
		+	==Computer Simulations Suggest That Prostate Enlargement Due to Benign Prostatic Hyperplasia Mechanically Impedes Prostate Cancer Growth==
		+
		+	{\| width="100%"
		+	\|
		+	'''Publication:''' [https://www.ncbi.nlm.nih.gov/pubmed/30617074 Proc Natl Acad Sci U S A. 2019 Jan 22;116(4):1152-61. PMID: 30617074] \| [https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6347698/pdf/pnas.201815735.pdf PDF]
		+
		+	'''Authors:''' Lorenzo G, Hughes TJR, Dominguez-Frojan P, Reali A, Gomez H.
		+
		+	'''Institution:''' Departamento de Matemáticas, Universidade da Coruña, Spain.
		+
		+	'''Abstract:''' Prostate cancer and benign prostatic hyperplasia are common genitourinary diseases in aging men. Both pathologies may coexist and share numerous similarities, which have suggested several connections or some interplay between them. However, solid evidence confirming their existence is lacking. Recent studies on extensive series of prostatectomy specimens have shown that tumors originating in larger prostates present favorable pathological features. Hence, large prostates may exert a protective effect against prostate cancer. In this work, we propose a mechanical explanation for this phenomenon. The mechanical stress fields that originate as tumors enlarge have been shown to slow down their dynamics. Benign prostatic hyperplasia contributes to these mechanical stress fields, hence further restraining prostate cancer growth. We derived a tissue-scale, patient-specific mechanically coupled mathematical model to qualitatively investigate the mechanical interaction of prostate cancer and benign prostatic hyperplasia. This model was calibrated by studying the deformation caused by each disease independently. Our simulations show that a history of benign prostatic hyperplasia creates mechanical stress fields in the prostate that impede prostatic tumor growth and limit its invasiveness. The technology presented herein may assist physicians in the clinical management of benign prostate hyperplasia and prostate cancer by predicting pathological outcomes on a tissue-scale, patient-specific basis.
		+
		+	"...used [http://www.slicer.org '''3D Slicer'''] to generate a triangular surface model of the prostate from the contours of the organ and the urethra drawn on the T2-weighted MR images, using the provided prostate segmentation as guidance."
	\|}		\|}

← Older revision		Revision as of 19:38, 12 February 2020
Line 1,930:		Line 1,930:

	"...Image registration to compute average shapes of the whole brain, hippocampus, and neocortex was performed using a python script, MeshLab software (www.meshlab.net/), and the SPHARM module of [http://www.slicer.org '''3D Slicer'''] software. "		"...Image registration to compute average shapes of the whole brain, hippocampus, and neocortex was performed using a python script, MeshLab software (www.meshlab.net/), and the SPHARM module of [http://www.slicer.org '''3D Slicer'''] software. "
		+	\|}
		+
		+	==Opposing CSF Hydrodynamic Trends Found in the Cerebral Aqueduct and Prepontine Cistern Following Shunt Treatment in Patients With Normal Pressure Hydrocephalus==
		+
		+	{\| width="100%"
		+	\|
		+	'''Publication:''' [https://www.ncbi.nlm.nih.gov/pubmed/30665428 Fluids Barriers CNS. 2019 Jan 22;16(1):2. PMID: 30665428] \| [https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6341759/pdf/12987_2019_Article_122.pdf PDF]
		+
		+	'''Authors:''' Hamilton RB, Scalzo F, Baldwin K, Dorn A, Vespa P, Hu X, Bergsneider M.
		+
		+	'''Institution:'''
		+	Neural Systems and Dynamics Laboratory, Department of Neurosurgery, The David Geffen School of Medicine, University of California-Los Angeles, Los Angeles, CA, USA.
		+
		+	'''Abstract:''' BACKGROUND:
		+	This study investigated cerebrospinal fluid (CSF) hydrodynamics using cine phase-contrast MRI in the cerebral aqueduct and the prepontine cistern between three distinct groups: pre-shunt normal pressure hydrocephalus (NPH) patients, post-shunt NPH patients, and controls. We hypothesized that the hyperdynamic flow of CSF through the cerebral aqueduct seen in NPH patients was due to a reduction in cisternal CSF volume buffering. Both hydrodynamic (velocity, flow, stroke volume) and peak flow latency (PFL) parameters were investigated.
		+
		+	METHODS:
		+	Scans were conducted on 30 pre-treatment patients ranging in age from 58 to 88 years along with an additional 12 controls. Twelve patients also received scans following either ventriculoatrial (VA) or ventriculoperitoneal (VP) shunt treatment (9 VP, 3 VA), ranging in age from 74 to 89 years with a mean follow up time of 6 months.
		+
		+	RESULTS:
		+	Significant differences in area, velocity, flow, and stroke volume for the cerebral aqueduct were found between the pre-treatment NPH group and the healthy controls. Shunting caused a significant decrease in both caudal and cranial mean flow and stroke volume in the cerebral aqueduct. No significant changes were found in the prepontine cistern between the pre-treatment group and healthy controls. For the PFL, no significant differences were seen in the cerebral aqueduct between any of the three groups; however, the prepontine cistern PFL was significantly decreased in the pre-treatment NPH group when compared to the control group.
		+
		+	CONCLUSIONS:
		+	Although several studies have quantified the changes in aqueductal flow between hydrocephalic groups and controls, few studies have investigated prepontine cistern flow. Our study was the first to investigate both regions in the same patients for NPH pre- and post- treatment. Following shunt treatment, the aqueductal CSF metrics decreased toward control values, while the prepontine cistern metrics trended up (not significantly) from the normal values established in this study. The opposing trend of the two locations suggests a redistribution of CSF pulsatility in NPH patients. Furthermore, the significantly decreased latency of the prepontine cisternal CSF flow suggests additional evidence for CSF pulsatility dysfunction.
		+
		+	"...For the nine patients that had pre- and post- treatment scans, the total lateral and third ventricle volumes were calculated ([http://www.slicer.org '''3D Slicer''']). "
	\|}		\|}

@@ Line 1,915: / Line 1,915: @@
 CONCLUSION:
 The results support our clinical observations that the ideal position of the lateralization sutures is the one which provides a physiological abduction position of the arytenoid cartilage. Considering these good results, the surgical indications for minimally invasive endoscopic arytenoid lateropexy may be extended.
 |}

@@ Line 1,677: / Line 1,677: @@
 {| width="100%"
+|
-'''Publication:''' [https://www.ncbi.nlm.nih.gov/pubmed/30683896 Sci Rep. 2019 Mar 13;9(1):4329. Nat Commun. 2019 Jan 25;10(1):428. PMID: 30683896] | [https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6347608/pdf/41467_2018_Article_8192.pdf PDF]
+'''Publication:''' [https://www.ncbi.nlm.nih.gov/pubmed/30683896 Nat Commun. 2019 Jan 25;10(1):428. PMID: 30683896] | [https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6347608/pdf/41467_2018_Article_8192.pdf PDF]
 '''Authors:''' Insinna C, Lu Q, Teixeira I, Harned A, Semler EM, Stauffer J, Magidson V, Tiwari A, Kenworthy AK, Narayan K, Westlake CJ.

@@ Line 1,671: / Line 1,671: @@
 LEVEL OF EVIDENCE:
 Level III, retrospective therapeutic trial.
 |}
@@ Line 1,684: / Line 1,706: @@
 '''Abstract:'''
- BACKGROUND:
+BACKGROUND:
 The reproducibility and non-redundancy of radiomic features are challenges in accelerating the clinical translation of radiomics. In this study, we focused on the robustness and non-redundancy of radiomic features extracted from computed tomography (CT) scans in hepatocellular carcinoma (HCC) patients with respect to different tumor segmentation methods.