Organ segmentation from full-size CT images using memory-efficient FCN

Chenglong Wang; Masahiro Oda; Kensaku Mori

doi:10.1117/12.2551024

16 March 2020 Organ segmentation from full-size CT images using memory-efficient FCN

Chenglong Wang, Masahiro Oda, Kensaku Mori

Author Affiliations +

Proceedings Volume 11314, Medical Imaging 2020: Computer-Aided Diagnosis; 113140I (2020) https://doi.org/10.1117/12.2551024
Event: SPIE Medical Imaging, 2020, Houston, Texas, United States

Abstract

In this work, we present a memory-efficient fully convolutional network (FCN) incorporated with several memory-optimized techniques to reduce the run-time GPU memory demand during training phase. In medical image segmentation tasks, subvolume cropping has become a common preprocessing. Subvolumes (or small patch volumes) were cropped to reduce GPU memory demand. However, small patch volumes capture less spatial context that leads to lower accuracy. As a pilot study, the purpose of this work is to propose a memory-efficient FCN which enables us to train the model on full size CT image directly without subvolume cropping, while maintaining the segmentation accuracy. We optimize our network from both architecture and implementation. With the development of computing hardware, such as graphics processing unit (GPU) and tensor processing unit (TPU), now deep learning applications is able to train networks with large datasets within acceptable time. Among these applications, semantic segmentation using fully convolutional network (FCN) also has gained a significant improvement against traditional image processing approaches in both computer vision and medical image processing fields. However, unlike general color images used in computer vision tasks, medical images have larger scales than color images such as 3D computed tomography (CT) images, micro CT images, and histopathological images. For training these medical images, the large demand of computing resource become a severe problem. In this paper, we present a memory-efficient FCN to tackle the high GPU memory demand challenge in organ segmentation problem from clinical CT images. The experimental results demonstrated that our GPU memory demand is about 40% of baseline architecture, parameter amount is about 30% of the baseline.

Conference Presentation

Citation Download Citation

Chenglong Wang, Masahiro Oda, and Kensaku Mori "Organ segmentation from full-size CT images using memory-efficient FCN", Proc. SPIE 11314, Medical Imaging 2020: Computer-Aided Diagnosis, 113140I (16 March 2020); https://doi.org/10.1117/12.2551024

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