Unified multi-scale feature abstraction for medical image segmentation

Xi Fang; Bo Du; Sheng Xu; Bradford J. Wood; Pingkun Yan

doi:10.1117/12.2549382

10 March 2020 Unified multi-scale feature abstraction for medical image segmentation

Xi Fang, Bo Du, Sheng Xu, Bradford J. Wood, Pingkun Yan

Proceedings Volume 11313, Medical Imaging 2020: Image Processing; 1131319 (2020) https://doi.org/10.1117/12.2549382
Event: SPIE Medical Imaging, 2020, Houston, Texas, United States

Abstract

Multi-scale contextual information is effective for pixel-level label prediction, i.e. image segmentation. However, such important information is only partially exploited in the existing methods. In this paper, we propose a new network architecture for unified multi-scale feature abstraction. The proposed network performs multi-scale analysis to the input image by using spatial pyramid pooling to obtain scene context information and abstract multi-scale features hierarchically. In addition, we present a new skip pathways to learn context information by fusing semantically similar features and develop a deep supervision mechanism for outputs in different scales. The proposed mechanisms relieve the gradient vanishing problem and enforce semantic feature learning. We extensively evaluated our method on the MICCAI 2017 Liver Tumor Segmentation (LiTS) Challenge Dataset and demonstrate highly competitive performance with single step operation and lightweight 2D networks.

Conference Presentation

Citation Download Citation

Xi Fang, Bo Du, Sheng Xu, Bradford J. Wood, and Pingkun Yan "Unified multi-scale feature abstraction for medical image segmentation", Proc. SPIE 11313, Medical Imaging 2020: Image Processing, 1131319 (10 March 2020); https://doi.org/10.1117/12.2549382

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