Organ at Risk

Organ-at-risk (OAR) segmentation in radiotherapy planning aims to accurately identify and delineate healthy tissues surrounding tumors to minimize radiation damage during cancer treatment. Current research heavily utilizes deep learning, employing architectures like U-Net and its variants, GANs, and novel approaches incorporating attention mechanisms and contrastive learning, to automate this process and improve consistency compared to manual delineation. This focus stems from the critical need to improve treatment efficacy and patient safety by reducing both the time-consuming nature of manual segmentation and the risk of human error. Improved OAR segmentation algorithms are leading to more precise radiotherapy plans and potentially better patient outcomes.

Papers

November 8, 2024

Machine learning for prediction of dose-volume histograms of organs-at-risk in prostate cancer from simple structure volume parameters
Saheli Saha, Debasmita Banerjee, Rishi Ram, Gowtham Reddy, Debashree Guha, Arnab Sarkar, Bapi Dutta, Moses ArunSingh S, Suman Chakraborty, Indranil Mallick
Machine Learning Prostate Cancer Dose Prediction Surface Parameterization FCM Deep Neural Network Organ at Risk Dose Volume Histogram

November 1, 2024

Deep learning-based auto-contouring of organs/structures-at-risk for pediatric upper abdominal radiotherapy
Mianyong Ding, Matteo Maspero, Annemieke S Littooij, Martine van Grotel, Raquel Davila Fajardo, Max M van Noesel, Marry M van den Heuvel-Eibrink, Geert O Janssens
Multi Organ Segmentation Patient Specific Inadequate Contour Encoding Human Organ Structural Damage Organ at Risk Pediatric Tumor Anatomical Sub Region

September 27, 2024

Towards Integrating Epistemic Uncertainty Estimation into the Radiotherapy Workflow
Marvin Tom Teichmann, Manasi Datar, Lisa Kratzke, Fernando Vega, Florin C. Ghesu
Uncertainty Estimation Epistemic Uncertainty Treatment Planning Organ at Risk

May 20, 2024

Quality assurance of organs-at-risk delineation in radiotherapy
Yihao Zhao, Cuiyun Yuan, Ying Liang, Yang Li, Chunxia Li, Man Zhao, Jun Hu, Wei Liu, Chenbin Liu
Automatic Segmentation Radiation Oncology Quality Control Boundary Delineation Contour Error Contour Tracking Organ at Risk

November 12, 2023

SegReg: Segmenting OARs by Registering MR Images and CT Annotations
Zeyu Zhang, Xuyin Qi, Bowen Zhang, Biao Wu, Hien Le, Bora Jeong, Zhibin Liao, Yunxiang Liu, Johan Verjans, Minh-Son To, Richard Hartley
Medical Image Recognition Normalization Method Organ at Risk

October 4, 2023

Fully Automatic Segmentation of Gross Target Volume and Organs-at-Risk for Radiotherapy Planning of Nasopharyngeal Carcinoma
Mehdi Astaraki, Simone Bendazzoli, Iuliana Toma-Dasu
Automatic Segmentation Treatment Planning Segmentation Algorithm Target Population Segmentation Target Target Volume Nasopharyngeal Carcinoma Organ at Risk Gross Tumor Volume

March 31, 2023

Comparing Adversarial and Supervised Learning for Organs at Risk Segmentation in CT images
Leonardo Crespi, Mattia Portanti, Daniele Loiacono
Generative Adversarial Network Supervised Learning CT Image Adversarial Text Human Organ GAN Generated Image Risk Segmentation Organ at Risk

March 1, 2022

A unified 3D framework for Organs at Risk Localization and Segmentation for Radiation Therapy Planning
Fernando Navarro, Guido Sasahara, Suprosanna Shit, Ivan Ezhov, Jan C. Peeken, Stephanie E. Combs, Bjoern H. Menze
Segmentation Based Approach Organ Segmentation 3D Representation Treatment Planning Medical Image Analysis Task Human Organ Organ at Risk Risk Localization

February 3, 2022

Enhancing Organ at Risk Segmentation with Improved Deep Neural Networks
Ilkin Isler, Curtis Lisle, Justin Rineer, Patrick Kelly, Damla Turgut, Jacob Ricci, Ulas Bagci
Neural Network Segmentation Dataset Biomedical Image Segmentation Human Organ Enhanced Segmentation Risk Segmentation Organ at Risk

December 28, 2021

SECP-Net: SE-Connection Pyramid Network of Organ At Risk Segmentation for Nasopharyngeal Carcinoma
Zexi Huang, Lihua Guo, Xin Yang, Sijuan Huang
Segmentation Performance Medical Image Segmentation Task Pyramid Network Human Organ Nasopharyngeal Carcinoma Risk Segmentation Organ at Risk

December 6, 2021

Separated Contrastive Learning for Organ-at-Risk and Gross-Tumor-Volume Segmentation with Limited Annotation
Jiacheng Wang, Xiaomeng Li, Yiming Han, Jing Qin, Liansheng Wang, Zhou Qichao
Contrastive Learning Limited Annotation Multi Organ Organ at Risk Gross Tumor Volume

Organ at Risk

Papers

Machine learning for prediction of dose-volume histograms of organs-at-risk in prostate cancer from simple structure volume parameters

Deep learning-based auto-contouring of organs/structures-at-risk for pediatric upper abdominal radiotherapy

Towards Integrating Epistemic Uncertainty Estimation into the Radiotherapy Workflow

Quality assurance of organs-at-risk delineation in radiotherapy

SegReg: Segmenting OARs by Registering MR Images and CT Annotations

Fully Automatic Segmentation of Gross Target Volume and Organs-at-Risk for Radiotherapy Planning of Nasopharyngeal Carcinoma

Comparing Adversarial and Supervised Learning for Organs at Risk Segmentation in CT images

A unified 3D framework for Organs at Risk Localization and Segmentation for Radiation Therapy Planning

Enhancing Organ at Risk Segmentation with Improved Deep Neural Networks

SECP-Net: SE-Connection Pyramid Network of Organ At Risk Segmentation for Nasopharyngeal Carcinoma

Separated Contrastive Learning for Organ-at-Risk and Gross-Tumor-Volume Segmentation with Limited Annotation