Presentation is loading. Please wait.

Presentation is loading. Please wait.

System Challenges in Image Analysis for Radiation Therapy Stephen M. Pizer Kenan Professor Medical Image Display & Analysis Group University.

Published byJaniya Venis Modified over 9 years ago

Similar presentations

Presentation on theme: "System Challenges in Image Analysis for Radiation Therapy Stephen M. Pizer Kenan Professor Medical Image Display & Analysis Group University."— Presentation transcript:

1 MIDAG@UNC System Challenges in Image Analysis for Radiation Therapy Stephen M. Pizer Kenan Professor Medical Image Display & Analysis Group University of North Carolina Co-authors: Edward L. Chaney, Julian G. Rosenman Credits to many others in UNC MIDAG Stephen M. Pizer Kenan Professor Medical Image Display & Analysis Group University of North Carolina Co-authors: Edward L. Chaney, Julian G. Rosenman Credits to many others in UNC MIDAG

2 MIDAG@UNC Objective: Segmentation in Radiation Treatment Planning & Delivery ä ä Target problems, in everyday RT ä ä Planning radiotherapy ä ä Segmentation of normal organs to be spared ä ä New patients ä ä Kidney, liver, head and neck ä ä Segmentation of regions implied by segmented organs: lymph levels ä ä Adaptive radiotherapy, incl. IGRT ä ä Segmentation of organs to be spared and target organ ä ä Day to day changes within a patient ä ä Male pelvic organs: bladder, prostate, rectum ä ä Target problems, in everyday RT ä ä Planning radiotherapy ä ä Segmentation of normal organs to be spared ä ä New patients ä ä Kidney, liver, head and neck ä ä Segmentation of regions implied by segmented organs: lymph levels ä ä Adaptive radiotherapy, incl. IGRT ä ä Segmentation of organs to be spared and target organ ä ä Day to day changes within a patient ä ä Male pelvic organs: bladder, prostate, rectum bladder, protsate, rectum

3 MIDAG@UNC Goal: Segmentation for Radiation Treatment Planning & Delivery ä ä Our objective: A whole new level of segmentation ability ä ä On tough images ä ä As good as humans in most cases ä ä The principle ä ä Make use of probability distribution of geometric variation: p(m) ä ä Make use of probability distribution of geometry-relative intensity patterns p(I | m) ä ä Posterior optimization ä ä arg max m p(m | I) = arg max m [log p(I | m) + log p(m) ] ä ä Our objective: A whole new level of segmentation ability ä ä On tough images ä ä As good as humans in most cases ä ä The principle ä ä Make use of probability distribution of geometric variation: p(m) ä ä Make use of probability distribution of geometry-relative intensity patterns p(I | m) ä ä Posterior optimization ä ä arg max m p(m | I) = arg max m [log p(I | m) + log p(m) ] Bladder CTs Prostate

4 MIDAG@UNC To achieve segmentation via Obtaining Training Data To achieve segmentation via p(m) and p(I | m): Obtaining Training Data ä ä Fitting m to training binary images ä ä arg min m f(m | binary I) = arg min m [image match penalty + geometric penalty] ä ä Tight fit of geometric model to binary is critical ä ä Extracting object-relative intensity patterns from corresponding CT images ä ä The tight fit of geometric model to binary makes regional intensity patterns more informative ä ä Fitting m to training binary images ä ä arg min m f(m | binary I) = arg min m [image match penalty + geometric penalty] ä ä Tight fit of geometric model to binary is critical ä ä Extracting object-relative intensity patterns from corresponding CT images ä ä The tight fit of geometric model to binary makes regional intensity patterns more informative

5 MIDAG@UNC Representing Representing m and I | m ä ä Principle: representation should support PCA ä ä Representing object geometry m ä ä M-rep: sheet of medial atoms ä ä Captures local twisting, bending, magnification of interior ä ä Unfamiliar to physicians ä ä Representing image pattern relative to geometry ä ä I | m = = I relative to m = RIQF(interior), RIQF(exterior) ä ä RIQF: regional intensity quantile function ä ä Unfamiliar to physicians ä ä Principle: representation should support PCA ä ä Representing object geometry m ä ä M-rep: sheet of medial atoms ä ä Captures local twisting, bending, magnification of interior ä ä Unfamiliar to physicians ä ä Representing image pattern relative to geometry ä ä I | m = = I relative to m = RIQF(interior), RIQF(exterior) ä ä RIQF: regional intensity quantile function ä ä Unfamiliar to physicians Prostate

6 MIDAG@UNC Segmentation Program ä ä Initialize pose of mean according to image landmarks ä Conjugate gradient optimization of over coefficients of 9 principal geodesics of ä Conjugate gradient optimization of log p(I | m) + log p(m) over coefficients of 9 principal geodesics of p(m) ä ä Objects are thereby restricted to credible shapes ä ä Initialize pose of mean according to image landmarks ä Conjugate gradient optimization of over coefficients of 9 principal geodesics of ä Conjugate gradient optimization of log p(I | m) + log p(m) over coefficients of 9 principal geodesics of p(m) ä ä Objects are thereby restricted to credible shapes

7 MIDAG@UNC System Challenges ä ä Challenges of doing the research within a clinical setting ä ä Challenges of getting the research results evaluated in a clinical context ä ä Challenges of clinical adoption of the research results ä ä Challenges of doing the research within a clinical setting ä ä Challenges of getting the research results evaluated in a clinical context ä ä Challenges of clinical adoption of the research results

8 MIDAG@UNC System Challenges in the Research ä Acquisition of image data of adequate quality ä Meet HIPAA regulations: anonymization ä Homogeneous, high resolution data sets ä Full volume of interest ä As artifact free as possible, or with typical artifacts ä Training cases from target population (with cancer) and from patients with normal anatomy ä Conversion of images and segmentations images and segmentations in RT planning and delivery system into research system ä Acquisition of image data of adequate quality ä Meet HIPAA regulations: anonymization ä Homogeneous, high resolution data sets ä Full volume of interest ä As artifact free as possible, or with typical artifacts ä Training cases from target population (with cancer) and from patients with normal anatomy ä Conversion of images and segmentations images and segmentations in RT planning and delivery system into research system

9 MIDAG@UNC ä ä High quality manual segmentations in RT planning and delivery system ä ä Consistency across training cases ä ä In adaptive RT: consistency with MD’s planning day ä ä Planning of RT: Include multi-expert variation ä ä Improved manual segmentation tools were developed ä ä High quality manual segmentations in RT planning and delivery system ä ä Consistency across training cases ä ä In adaptive RT: consistency with MD’s planning day ä ä Planning of RT: Include multi-expert variation ä ä Improved manual segmentation tools were developed System Challenges in the Research, cont.

10 MIDAG@UNC ä ä Need research-tolerant and interested physicians on the team ä ä Need physician input all along, without too heavily disappointing them with early results ä ä New approach was expected to, and did, take a decade to develop ä ä Obtaining a large number of careful, manual segmentations ä ä Need research-tolerant and interested physicians on the team ä ä Need physician input all along, without too heavily disappointing them with early results ä ä New approach was expected to, and did, take a decade to develop ä ä Obtaining a large number of careful, manual segmentations

11 MIDAG@UNC Evaluation experiments ä ä Retrospective ä ä Our data ä ä Other hospital’s data ä ä Prospective ä ä Within clinical practice, but not interfering with it ä ä “Jeopardy” that they might use the good results clinically during the test ä ä Complete their own segmentations first ä ä Retrospective ä ä Our data ä ä Other hospital’s data ä ä Prospective ä ä Within clinical practice, but not interfering with it ä ä “Jeopardy” that they might use the good results clinically during the test ä ä Complete their own segmentations first

12 MIDAG@UNC System Challenges in the Evaluation ä ä Access to software that is used clinically ä ä Adding objects with geometry, not just image slice contours (in one orientation), to RT system and its philosophy ä ä Providing segmentations with clinically useful measures of tolerance ä ä Hiding the image analysis details from the clinical user, while allowing access to them by the image analysis researcher ä ä Software’s robustness, reproducibility, user independence, speed (also for clinical use) ä ä Access to software that is used clinically ä ä Adding objects with geometry, not just image slice contours (in one orientation), to RT system and its philosophy ä ä Providing segmentations with clinically useful measures of tolerance ä ä Hiding the image analysis details from the clinical user, while allowing access to them by the image analysis researcher ä ä Software’s robustness, reproducibility, user independence, speed (also for clinical use)

13 MIDAG@UNC System Challenges in the Evaluation, cont. ä ä Need consensus performance standards ä ä No gold standard with real clinical material ä ä Comparisons of computer vs.human differences against inter-human or intra-human differences ä ä Means of generating synthetic but realistic cases with known truth ä ä Community-wide test case collections ä ä Need consensus performance standards ä ä No gold standard with real clinical material ä ä Comparisons of computer vs.human differences against inter-human or intra-human differences ä ä Means of generating synthetic but realistic cases with known truth ä ä Community-wide test case collections Case index distance Cf. human-to-human prostate agreement: 1.9mm average surface distance

14 MIDAG@UNC System Challenges of the Clinical Context ä ä Adding indications of non-credibility to segmentations; they may believe too readily ä ä Adding editing capability to computer generated segmentations ä ä Software continuing to change, during clinical tests and clinical use ä ä Adding indications of non-credibility to segmentations; they may believe too readily ä ä Adding editing capability to computer generated segmentations ä ä Software continuing to change, during clinical tests and clinical use

15 MIDAG@UNC ConclusionConclusion ä ä Research on IGT methods has not only technical and clinical challenges but also significant software system challenges

16 MIDAG@UNC

Download ppt "System Challenges in Image Analysis for Radiation Therapy Stephen M. Pizer Kenan Professor Medical Image Display & Analysis Group University."

Similar presentations

Surface contour scanning system

Surface contour scanning system
Patient information extraction in digitized X-ray imagery Hsien-Huang P. Wu Department of Electrical Engineering, National Yunlin University of Science.

Patient information extraction in digitized X-ray imagery Hsien-Huang P. Wu Department of Electrical Engineering, National Yunlin University of Science.
Independent Component Analysis Personal Viewpoint: Directions that maximize independence Motivating Context: Signal Processing “Blind Source Separation”

Independent Component Analysis Personal Viewpoint: Directions that maximize independence Motivating Context: Signal Processing “Blind Source Separation”
In the past few years the usage of conformal and IMRT treatments has been increasing rapidly. These treatments employ the use of tighter margins around.

In the past few years the usage of conformal and IMRT treatments has been increasing rapidly. These treatments employ the use of tighter margins around.
These improvements are in the context of automatic segmentations which are among the best found in the literature, exceeding agreement between experts.

These improvements are in the context of automatic segmentations which are among the best found in the literature, exceeding agreement between experts.
PET/CT Working Group Update Jayashree Kalpathy-Cramer Sandy Napel.

PET/CT Working Group Update Jayashree Kalpathy-Cramer Sandy Napel.
Background:  IMRT has become the choice of treatment for disease sites that require critical structure sparing such as head and neck cancer.  It has.

Background:  IMRT has become the choice of treatment for disease sites that require critical structure sparing such as head and neck cancer.  It has.
Effects of Sampling in IMRT Optimization by Ronald L. Rardin Professor of Industrial Engineering Purdue University West Lafayette, Indiana, USA Caesarea.

Effects of Sampling in IMRT Optimization by Ronald L. Rardin Professor of Industrial Engineering Purdue University West Lafayette, Indiana, USA Caesarea.
1 Skeletal Models Correspondence: Improved Interface, Organization, and Speed Clients: Prof. Stephen Pizer Comp.

1 Skeletal Models Correspondence: Improved Interface, Organization, and Speed Clients: Prof. Stephen Pizer Comp.
Multiscale Profile Analysis Joshua Stough MIDAG PI: Chaney, Pizer July, 2003 Joshua Stough MIDAG PI: Chaney, Pizer July, 2003.

Multiscale Profile Analysis Joshua Stough MIDAG PI: Chaney, Pizer July, 2003 Joshua Stough MIDAG PI: Chaney, Pizer July, 2003.
Medial Object Shape Representations for Image Analysis & Object Synthesis Stephen M. Pizer Kenan Professor Medical Image Display & Analysis Group.

Medial Object Shape Representations for Image Analysis & Object Synthesis Stephen M. Pizer Kenan Professor Medical Image Display & Analysis Group.
University of North Carolina Comparison of Human and M-rep Kidneys Segmented from CT Images James Chen, Gregg Tracton, Manjari Rao, Sarang Joshi, Steve.

University of North Carolina Comparison of Human and M-rep Kidneys Segmented from CT Images James Chen, Gregg Tracton, Manjari Rao, Sarang Joshi, Steve.
Medical Image Synthesis via Monte Carlo Simulation James Z. Chen, Stephen M. Pizer, Edward L. Chaney, Sarang Joshi Medical Image Display & Analysis Group,

Medical Image Synthesis via Monte Carlo Simulation James Z. Chen, Stephen M. Pizer, Edward L. Chaney, Sarang Joshi Medical Image Display & Analysis Group,
Session - 25 MULTIDATABASE CASE Electronic Health Matakuliah: M0184 / Pengolahan Data Distribusi Tahun: 2005 Versi:

Session - 25 MULTIDATABASE CASE Electronic Health Matakuliah: M0184 / Pengolahan Data Distribusi Tahun: 2005 Versi:
Clustering on Image Boundary Regions for Deformable Model Segmentation Joshua Stough, Stephen M. Pizer, Edward L. Chaney, Manjari Rao Medical Image Display.

Clustering on Image Boundary Regions for Deformable Model Segmentation Joshua Stough, Stephen M. Pizer, Edward L. Chaney, Manjari Rao Medical Image Display.
Yujun Guo Kent State University August 13. 2003 PRESENTATION A Binarization Approach for CT-MR Registration Using Normalized Mutual Information.

Yujun Guo Kent State University August PRESENTATION A Binarization Approach for CT-MR Registration Using Normalized Mutual Information.
The Uses of Object Shape from Images in Medicine Stephen M. Pizer Kenan Professor Medical Image Display & Analysis Group University of North.

The Uses of Object Shape from Images in Medicine Stephen M. Pizer Kenan Professor Medical Image Display & Analysis Group University of North.
12-Apr-20071 CSCE790T Medical Image Processing University of South Carolina Department of Computer Science 3D Active Shape Models Integrating Robust Edge.

12-Apr CSCE790T Medical Image Processing University of South Carolina Department of Computer Science 3D Active Shape Models Integrating Robust Edge.
Clustering on Local Appearance for Deformable Model Segmentation Joshua V. Stough, Robert E. Broadhurst, Stephen M. Pizer, Edward L. Chaney MIDAG, UNC-Chapel.

Clustering on Local Appearance for Deformable Model Segmentation Joshua V. Stough, Robert E. Broadhurst, Stephen M. Pizer, Edward L. Chaney MIDAG, UNC-Chapel.
Interpolation Snakes Work by Silviu Minut. Ultrasound image has noisy and broken boundaries Left ventricle of dog heart Geodesic contour moves to smoothly.

Interpolation Snakes Work by Silviu Minut. Ultrasound image has noisy and broken boundaries Left ventricle of dog heart Geodesic contour moves to smoothly.

Similar presentations

Ads by Google