arXiv 2411.013902025-02-18cs.CVeess.IV

一种用于儿童脑肿瘤分割的新逻辑

A New Logic For Pediatric Brain Tumor Segmentation

1 Northwestern University, Feinberg School of Medicine, Chicago IL, USA 2 Children’s National Hospital, Sheikh Zayed Institute for Pediatric Surgical Innovation, Washington, DC, USA 3 Ann \& Robert H Lurie Children's Hospital of Chicago, Chicago, IL, USA

Max Bengtsson, Elif Keles, Gorkem Durak, Syed Anwar, Yuri S. Velichko, Marius G. Linguraru, Angela J. Waanders, Ulas Bagci

DOI：: 10.1109/ISBI60581.2025.10980809

阅读 PDF

arXiv 页面 ↗HTML 原文 ↗

AI总结：

本文受放射专家分割策略启发提出儿童脑肿瘤深度学习分割模型，在多数据集测试中性能优于现有SOTA，泛化性优异，可为儿童脑肿瘤诊疗相关分割任务提供更精准的支持。

摘要：

本文提出了一种受放射科专家分割策略启发、基于深度学习架构的儿童脑肿瘤分割新方法。该模型可划分4种不同的肿瘤标签，在预留的PED BraTS 2024测试集（即BraTS推出的儿童脑肿瘤数据集）上完成基准测试。此外，我们使用来自CBTN（儿童脑肿瘤网络）的30例患者全新外部数据集，依照PED BraTS 2024指南标注，同时结合2023年BraTS成人胶质瘤数据集，将所提模型与当前最优（SOTA）模型进行性能对比，选取PED BraTS 2023挑战赛的获胜算法作为SOTA对比模型。所提算法在CBTN测试数据上的平均Dice得分为0.642，HD95为73.0毫米，优于SOTA模型的0.626 Dice得分与84.0毫米HD95结果。同时该模型泛化性优异，在BraTS 2023成人胶质瘤数据集上的全肿瘤分割Dice得分达0.877，超过现有SOTA水平。结果表明，该模型朝着实现更精准的儿童脑肿瘤分割迈出了一步，对疗效评估与患者病程监测至关重要。项目源代码可在https://github.com/NUBagciLab/Pediatric-Brain-Tumor-Segmentation-Model获取。

英文摘要：

In this paper, we present a novel approach for segmenting pediatric brain tumors using a deep learning architecture, inspired by expert radiologists' segmentation strategies. Our model delineates four distinct tumor labels and is benchmarked on a held-out PED BraTS 2024 test set (i.e., pediatric brain tumor datasets introduced by BraTS). Furthermore, we evaluate our model's performance against the state-of-the-art (SOTA) model using a new external dataset of 30 patients from CBTN (Children's Brain Tumor Network), labeled in accordance with the PED BraTS 2024 guidelines and 2023 BraTS Adult Glioma dataset. We compare segmentation outcomes with the winning algorithm from the PED BraTS 2023 challenge as the SOTA model. Our proposed algorithm achieved an average Dice score of 0.642 and an HD95 of 73.0 mm on the CBTN test data, outperforming the SOTA model, which achieved a Dice score of 0.626 and an HD95 of 84.0 mm. Moreover, our model exhibits strong generalizability, attaining a 0.877 Dice score in whole tumor segmentation on the BraTS 2023 Adult Glioma dataset, surpassing existing SOTA. Our results indicate that the proposed model is a step towards providing more accurate segmentation for pediatric brain tumors, which is essential for evaluating therapy response and monitoring patient progress. Our source code is available at https://github.com/NUBagciLab/Pediatric-Brain-Tumor-Segmentation-Model.

机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。