Improved organs at risk segmentation based on modified U-Net with self-attention and consistency regularisation

IF 8.4 2区 计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Maksym Manko, Anton Popov, Juan Manuel Gorriz, Javier Ramirez
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引用次数: 0

Abstract

Cancer is one of the leading causes of death in the world, with radiotherapy as one of the treatment options. Radiotherapy planning starts with delineating the affected area from healthy organs, called organs at risk (OAR). A new approach to automatic OAR segmentation in the chest cavity in Computed Tomography (CT) images is presented. The proposed approach is based on the modified U-Net architecture with the ResNet-34 encoder, which is the baseline adopted in this work. The new two-branch CS-SA U-Net architecture is proposed, which consists of two parallel U-Net models in which self-attention blocks with cosine similarity as query-key similarity function (CS-SA) blocks are inserted between the encoder and decoder, which enabled the use of consistency regularisation. The proposed solution demonstrates state-of-the-art performance for the problem of OAR segmentation in CT images on the publicly available SegTHOR benchmark dataset in terms of a Dice coefficient (oesophagus—0.8714, heart—0.9516, trachea—0.9286, aorta—0.9510) and Hausdorff distance (oesophagus—0.2541, heart—0.1514, trachea—0.1722, aorta—0.1114) and significantly outperforms the baseline. The current approach is demonstrated to be viable for improving the quality of OAR segmentation for radiotherapy planning.

Abstract Image

基于自关注和一致性正则化的改良 U-Net 改进了风险器官的划分
癌症是世界上最主要的死亡原因之一,放疗是其中一种治疗方法。放疗计划首先要从健康器官(称为危险器官(OAR))中划分出受影响的区域。本文介绍了一种在计算机断层扫描(CT)图像中自动分割胸腔内危险器官的新方法。所提出的方法基于改进的 U-Net 架构和 ResNet-34 编码器,这是本研究采用的基线。提出了新的双分支 CS-SA U-Net 架构,该架构由两个并行 U-Net 模型组成,其中在编码器和解码器之间插入了具有余弦相似性的自注意块作为查询键相似性函数(CS-SA)块,从而实现了一致性正则化的使用。在公开的 SegTHOR 基准数据集上,针对 CT 图像中的 OAR 分割问题,所提出的解决方案在 Dice 系数(食道-0.8714、心脏-0.9516、气管-0.9286、主动脉-0.9510)和 Hausdorff 距离(食道-0.2541、心脏-0.1514、气管-0.1722、主动脉-0.1114)方面都表现出了最先进的性能,并明显优于基线。事实证明,目前的方法可用于提高放疗计划的 OAR 分割质量。
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来源期刊
CAAI Transactions on Intelligence Technology
CAAI Transactions on Intelligence Technology COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE-
CiteScore
11.00
自引率
3.90%
发文量
134
审稿时长
35 weeks
期刊介绍: CAAI Transactions on Intelligence Technology is a leading venue for original research on the theoretical and experimental aspects of artificial intelligence technology. We are a fully open access journal co-published by the Institution of Engineering and Technology (IET) and the Chinese Association for Artificial Intelligence (CAAI) providing research which is openly accessible to read and share worldwide.
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