Image Quality and Segmentation.
Gargi Pednekar,Jayaram K. Udupa,David J. McLaughlin,Xingyu Wu,Yubing Tong,Charles B. Simone,Joseph Camaratta,Drew A. Torigian +7 more
- 01 Feb 2018
- Vol. 10576
29
TL;DR: A set of key quality criteria that influence segmentation (global and regional): posture deviations, image noise, beam hardening artifacts (streak artifacts), shape distortion, presence of pathology, object intensity deviation, and object contrast are devised.
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Abstract: Algorithms for image segmentation (including object recognition and delineation) are influenced by the quality of object appearance in the image and overall image quality. However, the issue of how to perform segmentation evaluation as a function of these quality factors has not been addressed in the literature. In this paper, we present a solution to this problem. We devised a set of key quality criteria that influence segmentation (global and regional): posture deviations, image noise, beam hardening artifacts (streak artifacts), shape distortion, presence of pathology, object intensity deviation, and object contrast. A trained reader assigned a grade to each object for each criterion in each study. We developed algorithms based on logical predicates for determining a 1 to 10 numeric quality score for each object and each image from reader-assigned quality grades. We analyzed these object and image quality scores (OQS and IQS, respectively) in our data cohort by gender and age. We performed recognition and delineation of all objects using recent adaptations [8, 9] of our Automatic Anatomy Recognition (AAR) framework [6] and analyzed the accuracy of recognition and delineation of each object. We illustrate our method on 216 head & neck and 211 thoracic cancer computed tomography (CT) studies.
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Citations
AnatomyNet: Deep learning for fast and fully automated whole-volume segmentation of head and neck anatomy
Wentao Zhu,Yufang Huang,Liang Zeng,Xuming Chen,Yong Liu,Zhen Qian,Nan Du,Wei Fan,Xiaohui Xie +8 more
TL;DR: An end-to-end, atlas-free three-dimensional convolutional deep learning framework for fast and fully automated whole-volume HaN anatomy segmentation and demonstrates that the proposed model can improve segmentation accuracy and simplify the autosegmentation pipeline.
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Clinically applicable deep learning framework for organs at risk delineation in CT images
Hao Tang,Xuming Chen,Yang Liu,Zhipeng Lu,Junhua You,Mingzhou Yang,Shengyu Yao,Guoqi Zhao,Yi Xu,Tingfeng Chen,Yong Liu,Xiaohui Xie +11 more
TL;DR: A new deep learning-based method for delineating organs in the area of head and neck performs faster and more accurately than human experts, significantly outperforming human experts and the previous state-of-the-art method.
179
AnatomyNet: Deep Learning for Fast and Fully Automated Whole-volume Segmentation of Head and Neck Anatomy.
Wentao Zhu,Yufang Huang,Liang Zeng,Xuming Chen,Yong Liu,Zhen Qian,Nan Du,Wei Fan,Xiaohui Xie +8 more
TL;DR: In this article, a 3D U-Net-based model, called AnatomyNet, was proposed to segment nine anatomies from head and neck CT images in an end-to-end fashion.
128
AnatomyNet: Deep 3D Squeeze-and-excitation U-Nets for fast and fully automated whole-volume anatomical segmentation
TL;DR: This work proposes an end-to-end, fast and fully automated deep convolutional network, AnatomyNet, for accurate and whole-volume HaN anatomical segmentation, which outperforms previous state-of-the-art methods on the benchmark dataset.
38
AAR-RT - A system for auto-contouring organs at risk on CT images for radiation therapy planning: Principles, design, and large-scale evaluation on head-and-neck and thoracic cancer cases.
Xingyu Wu,Jayaram K. Udupa,Yubing Tong,Dewey Odhner,Gargi Pednekar,Charles B. Simone,David J. McLaughlin,Chavanon Apinorasethkul,Ontida Apinorasethkul,John N. Lukens,Dimitris Mihailidis,Geraldine Shammo,Paul A. James,Akhil Tiwari,Lisa Wojtowicz,Joseph Camaratta,Drew A. Torigian +16 more
TL;DR: This paper extended the previous body‐wide Automatic Anatomy Recognition (AAR) framework to RT planning of OARs in the head and neck (H&N) and thoracic body regions, and devised a method to find an optimal hierarchy for each body region.
33
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CAVASS: A Computer-Assisted Visualization and Analysis Software System
George J. Grevera,George J. Grevera,Jayaram K. Udupa,Dewey Odhner,Ying Zhuge,Andre Souza,Tad Iwanaga,Shipra Mishra +7 more
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Knowledge-Based Auto Contouring for Radiation Therapy: Challenges in Standardizing Object Definitions, Ground Truth Delineations, Object Quality, and Image Quality
Xingyu Wu,Jayaram K. Udupa,Dewey Odhner,Yubing Tong,David J. McLaughlin,Gargi Pednekar,Charles B. Simone,Joseph Camaratta,Drew A. Torigian +8 more
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