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Updated: Jul 29, 2025

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Published on: November 30, 2022

对深度学习模型的全面评估,用于对异质计算机断层扫描图像的自动风险器官细分,用于腹部辐射治疗

Wenjun Liao¹, Xiangde Luo², Yuan He³

¹Department of Radiation Oncology, Radiation Oncology Key Laboratory of Sichuan Province, Sichuan Clinical Research Center for Cancer, Sichuan Cancer Hospital & Institute, Sichuan Cancer Center, Affiliated Cancer Hospital of University of Electronic Science and Technology of China, Chengdu, China.

International journal of radiation oncology, biology, physics

|May 27, 2023

概括

一个新的深度学习模型AbsegNet准确地划定了辐射治疗规划的风险器官 (OAR). 它的强大性能促进了自动化治疗工作流程,在各种数据集中显示了临床适用性.

科学领域:

医疗成像医学成像
人工智能的人工智能
辐射瘤学辐射瘤学

背景情况:

准确地划分有风险的器官 (OAR) 对于有效的放射治疗计划至关重要.
目前用于OAR轮的方法可能耗时,并且受观察者之间的变化影响.
自动化这个过程可以显著提高辐射瘤学工作流程的效率和一致性.

研究的目的:

开发和验证AbsegNet,这是一个深度学习模型,用于在腹部恶性瘤中准确地进行OAR轮.
评估模型在来自多个机构的各种数据集中的表现.
评估AbsegNet生成的轮在放射治疗规划中的临床适用性.

主要方法:

544个CT扫描在三个数据集中的回顾性收集.
使用培训/测试分割开发AbsegNet (300/128个案例).
在两个独立的队列 (n=24,n=20) 上进行外部验证,并在两个进一步的队列 (n=40,n=32) 上使用子相似系数,豪斯多夫距离和体积修订度进行临床准确性评估.

主要成果:

在验证队列中,AbsegNet实现了高平均子相似系数 (85.65%-88.04%) 和具有竞争力的豪斯多夫距离.
该模型的性能优于几个已建立的深度学习架构 (SwinUNETR,DeepLabV3+,注意力-UNet,UNet,3D-UNet).
临床评估显示,87.5%的OAR没有或仅有轻微的修订,只有15%需要对结肠和小肠轮进行重大修订.

结论:

AbsegNet是一个新的深度学习模型,用于在各种数据集上划分OAR.
该模型展示了准确和强大的轮,证明了临床适用性.
AbsegNet有可能显著地促进和改进放射治疗规划工作流程.

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Computed Tomography

Computed Tomography

Tomography refers to imaging by sections. Computed tomography (CT) is a non-invasive imaging technique that uses computers to analyze several cross-sectional X-rays to reveal minute details about structures in the body.
The technique was invented in the 1970s and is based on the principle that as X-rays pass through the body, they are absorbed or reflected at different levels. In the technique, a patient lies on a motorized platform while a computerized axial tomography (CAT) scanner rotates...