SegRap2023: A Benchmark of Organs-at-Risk and Gross Tumor Volume Segmentation for Radiotherapy Planning of Nasopharyngeal Carcinoma

• URL: http://arxiv.org/abs/2312.09576v1
• Date: Fri, 15 Dec 2023 07:08:38 GMT
• Title: SegRap2023: A Benchmark of Organs-at-Risk and Gross Tumor Volume Segmentation for Radiotherapy Planning of Nasopharyngeal Carcinoma
• Authors: Xiangde Luo, Jia Fu, Yunxin Zhong, Shuolin Liu, Bing Han, Mehdi Astaraki, Simone Bendazzoli, Iuliana Toma-Dasu, Yiwen Ye, Ziyang Chen, Yong Xia, Yanzhou Su, Jin Ye, Junjun He, Zhaohu Xing, Hongqiu Wang, Lei Zhu, Kaixiang Yang, Xin Fang, Zhiwei Wang, Chan Woong Lee, Sang Joon Park, Jaehee Chun, Constantin Ulrich, Klaus H. Maier-Hein, Nchongmaje Ndipenoch, Alina Miron, Yongmin Li, Yimeng Zhang, Yu Chen, Lu Bai, Jinlong Huang, Chengyang An, Lisheng Wang, Kaiwen Huang, Yunqi Gu, Tao Zhou, Mu Zhou, Shichuan Zhang, Wenjun Liao, Guotai Wang, Shaoting Zhang
• Abstract summary: delineation of Gross Tumor Volumes (GTVs) and Organs-At-Risk (OARs) is crucial in radiation treatment. The SegRap2023 challenge was organized in conjunction with MICCAI2023 and presented a large-scale benchmark for OAR and GTV segmentation. We detail the challenge and analyze the solutions of all participants.
• Score: 45.15178196643517
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Radiation therapy is a primary and effective NasoPharyngeal Carcinoma (NPC) treatment strategy. The precise delineation of Gross Tumor Volumes (GTVs) and Organs-At-Risk (OARs) is crucial in radiation treatment, directly impacting patient prognosis. Previously, the delineation of GTVs and OARs was performed by experienced radiation oncologists. Recently, deep learning has achieved promising results in many medical image segmentation tasks. However, for NPC OARs and GTVs segmentation, few public datasets are available for model development and evaluation. To alleviate this problem, the SegRap2023 challenge was organized in conjunction with MICCAI2023 and presented a large-scale benchmark for OAR and GTV segmentation with 400 Computed Tomography (CT) scans from 200 NPC patients, each with a pair of pre-aligned non-contrast and contrast-enhanced CT scans. The challenge's goal was to segment 45 OARs and 2 GTVs from the paired CT scans. In this paper, we detail the challenge and analyze the solutions of all participants. The average Dice similarity coefficient scores for all submissions ranged from 76.68\% to 86.70\%, and 70.42\% to 73.44\% for OARs and GTVs, respectively. We conclude that the segmentation of large-size OARs is well-addressed, and more efforts are needed for GTVs and small-size or thin-structure OARs. The benchmark will remain publicly available here: https://segrap2023.grand-challenge.org

Related papers

• Cancer-Net PCa-Seg: Benchmarking Deep Learning Models for Prostate Cancer Segmentation Using Synthetic Correlated Diffusion Imaging [65.83291923029985]
  Prostate cancer (PCa) is the most prevalent cancer among men in the United States, accounting for nearly 300,000 cases, 29% of all diagnoses and 35,000 total deaths in 2024. Traditional screening methods such as prostate-specific antigen (PSA) testing and magnetic resonance imaging (MRI) have been pivotal in diagnosis, but have faced limitations in specificity and generalizability. We employ several state-of-the-art deep learning models, including U-Net, SegResNet, Swin UNETR, Attention U-Net, and LightM-UNet, to segment PCa lesions from a 200 CDI$
  arXiv  Detail & Related papers  (2025-01-15T22:23:41Z)
• Deep Learning for Longitudinal Gross Tumor Volume Segmentation in MRI-Guided Adaptive Radiotherapy for Head and Neck Cancer [4.358109501717511]
  Accurate segmentation of gross tumor volume (GTV) is essential for effective MRI-guided adaptive radiotherapy (MRgART) in head and neck cancer. In this study, we tackled the challenges of both pre-radiotherapy (pre-RT) and mid-radiotherapy (mid-RT) tumor volume segmentation. We presented a collection of DL models that could facilitate GTV segmentation in MRgART, offering the potential to streamline radiation oncology.
  arXiv  Detail & Related papers  (2024-12-01T03:57:18Z)
• UMambaAdj: Advancing GTV Segmentation for Head and Neck Cancer in MRI-Guided RT with UMamba and nnU-Net ResEnc Planner [0.04924932828166548]
  Magnetic Resonance Imaging (MRI) plays a crucial role in adaptive radiotherapy for head and neck cancer (HNC) due to its superior soft-tissue contrast. accurately segmenting the gross tumor volume (GTV), which includes both the primary tumor (GTVp) and lymph nodes (GTVn) remains challenging. Recently, two deep learning segmentation innovations have shown great promise: UMamba, which effectively captures long-range dependencies, and the nnU-Net Residual (ResEnc) which enhances feature extraction through multistage residual blocks.
  arXiv  Detail & Related papers  (2024-10-16T18:26:27Z)
• Two Stage Segmentation of Cervical Tumors using PocketNet [3.7024689582067536]
  This work applied a novel deep-learning model (PocketNet) to segment the cervix, vagina, uterus, and tumor(s) on T2w MRI. PocketNet achieved a mean Dice-Sorensen similarity coefficient (DSC) exceeding 70% for tumor segmentation and 80% for organ segmentation.
  arXiv  Detail & Related papers  (2024-09-17T17:48:12Z)
• Brain Tumor Segmentation (BraTS) Challenge 2024: Meningioma Radiotherapy Planning Automated Segmentation [47.119513326344126]
  The BraTS-MEN-RT challenge aims to advance automated segmentation algorithms using the largest known multi-institutional dataset of radiotherapy planning brain MRIs. Each case includes a defaced 3D post-contrast T1-weighted radiotherapy planning MRI in its native acquisition space. Target volume annotations adhere to established radiotherapy planning protocols.
  arXiv  Detail & Related papers  (2024-05-28T17:25:43Z)
• Advanced Tumor Segmentation in Medical Imaging: An Ensemble Approach for BraTS 2023 Adult Glioma and Pediatric Tumor Tasks [0.8184931154670512]
  This study outlines our methodology for segmenting tumors in the context of two distinct tasks from the BraTS 2023 challenge: Adult Glioma and Pediatric Tumors. Our approach leverages two encoder-decoder-based CNN models, namely SegResNet and MedNeXt, for segmenting three distinct subregions of tumors. Our proposed approach achieves third place in the BraTS 2023 Adult Glioma Challenges with an average of 0.8313 and 36.38 Dice and HD95 scores on the test set, respectively.
  arXiv  Detail & Related papers  (2024-03-14T10:37:41Z)
• Recurrence-free Survival Prediction under the Guidance of Automatic Gross Tumor Volume Segmentation for Head and Neck Cancers [8.598790229614071]
  We developed an automated primary tumor (GTVp) and lymph nodes (GTVn) segmentation method. We extracted radiomics features from the segmented tumor volume and constructed a multi-modality tumor recurrence-free survival (RFS) prediction model.
  arXiv  Detail & Related papers  (2022-09-22T18:44:57Z)
• Lung-Originated Tumor Segmentation from Computed Tomography Scan (LOTUS) Benchmark [48.30502612686276]
  Lung cancer is one of the deadliest cancers, and its effective diagnosis and treatment depend on the accurate delineation of the tumor. Human-centered segmentation, which is currently the most common approach, is subject to inter-observer variability. The 2018 VIP Cup started with a global engagement from 42 countries to access the competition data. In a nutshell, all the algorithms proposed during the competition, are based on deep learning models combined with a false positive reduction technique.
  arXiv  Detail & Related papers  (2022-01-03T03:06:38Z)
• A Global Benchmark of Algorithms for Segmenting Late Gadolinium-Enhanced Cardiac Magnetic Resonance Imaging [90.29017019187282]
  " 2018 Left Atrium Challenge" using 154 3D LGE-MRIs, currently the world's largest cardiac LGE-MRI dataset. Analyse of the submitted algorithms using technical and biological metrics was performed. Results show the top method achieved a dice score of 93.2% and a mean surface to a surface distance of 0.7 mm.
  arXiv  Detail & Related papers  (2020-04-26T08:49:17Z)
• VerSe: A Vertebrae Labelling and Segmentation Benchmark for Multi-detector CT Images [121.31355003451152]
  Large Scale Vertebrae Challenge (VerSe) was organised in conjunction with the International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI) in 2019 and 2020. We present the the results of this evaluation and further investigate the performance-variation at vertebra-level, scan-level, and at different fields-of-view.
  arXiv  Detail & Related papers  (2020-01-24T21:09:18Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.