改进用于胃肠道分割手术的深度学习模型

Hong Zhou, Yan Lou, Jize Xiong, Yixu Wang, Yuxiang Liu
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摘要

2019 年,全球约有 500 万人被诊断出患有胃肠道癌症,其中约有一半符合放疗条件。由于 MR-Linacs 等新技术需要手动分割过程,这种对许多患者至关重要的治疗面临挑战。该项目由华盛顿大学麦迪逊分校卡本癌症中心(UW-Madison Carbone Cancer Center)支持,旨在利用深度学习自动分割核磁共振扫描中的胃和肠。Unet2.5D 模型,特别是 Unet2.5D(Se-ResNet50),已经取得了可喜的成果,骰子系数达到了 0.848。该模型的成功实施可以大大加快治疗速度,在对肿瘤进行更高的辐射剂量的同时,最大限度地减少对健康组织的照射,最终改善患者护理和长期癌症控制。
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Improvement of Deep Learning Model for Gastrointestinal Tract Segmentation Surgery
In 2019, approximately 5 million individuals were diagnosed with gastrointestinal tract cancer globally, with about half eligible for radiation therapy. This treatment, crucial for many patients, faces challenges due to the manual segmentation process required in newer technologies like MR-Linacs. This project, supported by the UW-Madison Carbone Cancer Center, aims to automate the segmentation of stomach and intestines in MRI scans using deep learning. The Unet2.5D model, specifically Unet2.5D(Se-ResNet50), has shown promising results, achieving a Dice Coefficient of 0.848. Successful implementation of this model could significantly expedite treatments, enabling higher radiation doses to tumors while minimizing exposure to healthy tissues, ultimately improving patient care and long-term cancer control.
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