A Transformer-Based Network for Anisotropic 3D Medical Image Segmentation

被引：24

作者：

Guo, Danfeng ^{[1
]}

Terzopoulos, Demetri ^{[1
,2
]}

机构：

[1] Univ Calif Los Angeles, Los Angeles, CA 90095 USA

[2] VoxelCloud Inc, Los Angeles, CA USA

来源：

2020 25TH INTERNATIONAL CONFERENCE ON PATTERN RECOGNITION (ICPR) | 2021年

关键词：

D O I：

10.1109/ICPR48806.2021.9411990

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Imaging anisotropy poses a critical challenge in applying deep learning models to 3D medical image analysis. Anisotropy downgrades model performance, especially when slice spacing varies significantly between training and clinical datasets. We propose a transformer-based model to tackle the anisotropy problem. It is adaptable to different levels of anisotropy and is computationally efficient. Our model outperforms baseline models in 3D lung cancer segmentation experiments.

引用

页码：8857 / 8861

页数：5

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