Multiple organ segmentation framework for brain metastasis radiotherapy
Radiotherapy is a preferred treatment for brain metastases, which kills cancer cells via high doses of radiation meanwhile hardly avoiding damage to surrounding healthy cells. Therefore, the delineation of organs-at-risk (OARs) is vital in treatment planning to minimize radiation-induced toxicity. H...
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Published in | Computers in biology and medicine Vol. 177; p. 108637 |
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Main Authors | , , , , , , , , |
Format | Journal Article |
Language | English |
Published |
United States
Elsevier Ltd
01.07.2024
Elsevier Limited |
Subjects | |
Online Access | Get full text |
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Summary: | Radiotherapy is a preferred treatment for brain metastases, which kills cancer cells via high doses of radiation meanwhile hardly avoiding damage to surrounding healthy cells. Therefore, the delineation of organs-at-risk (OARs) is vital in treatment planning to minimize radiation-induced toxicity. However, the following aspects make OAR delineation a challenging task: extremely imbalanced organ sizes, ambiguous boundaries, and complex anatomical structures. To alleviate these challenges, we imitate how specialized clinicians delineate OARs and present a novel cascaded multi-OAR segmentation framework, called OAR-SegNet. OAR-SegNet comprises two distinct levels of segmentation networks: an Anatomical-Prior-Guided network (APG-Net) and a Point-Cloud-Guided network (PCG-Net). Specifically, APG-Net handles segmentation for all organs, where multi-view segmentation modules and a deep prior loss are designed under the guidance of prior knowledge. After APG-Net, PCG-Net refines small organs through the mini-segmentation and the point-cloud alignment heads. The mini-segmentation head is further equipped with the deep prior feature. Extensive experiments were conducted to demonstrate the superior performance of the proposed method compared to other state-of-the-art medical segmentation methods.
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•Our OAR-SegNet innovatively combines prior knowledge and point-cloud alignment.•Prior knowledge is applied in RoI extraction, loss function design and feature learning.•Point-cloud alignment serves as an extra shape constraint to refine small organs. |
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Bibliography: | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23 |
ISSN: | 0010-4825 1879-0534 |
DOI: | 10.1016/j.compbiomed.2024.108637 |