Open Access

ARTICLE

Inner Cascaded U²-Net: An Improvement to Plain Cascaded U-Net

Wenbin Wu¹, Guanjun Liu^1,*, Kaiyi Liang², Hui Zhou²

1 Tongji University, Shanghai, 201804, China
2 Jiading District Central Hospital Affiliated to Shanghai University of Medicine and Health Sciences, Shanghai, 201800, China

* Corresponding Author: Guanjun Liu. Email:

(This article belongs to the Special Issue: Deep Learning based Computational Methods for Abnormality Detection in Human Medical Images)

Computer Modeling in Engineering & Sciences 2023, 134(2), 1323-1335. https://doi.org/10.32604/cmes.2022.020428

Received 23 November 2021; Accepted 14 March 2022; Issue published 31 August 2022

Abstract

Deep neural networks are now widely used in the medical image segmentation field for their performance superiority and no need of manual feature extraction. U-Net has been the baseline model since the very beginning due to a symmetrical U-structure for better feature extraction and fusing and suitable for small datasets. To enhance the segmentation performance of U-Net, cascaded U-Net proposes to put two U-Nets successively to segment targets from coarse to fine. However, the plain cascaded U-Net faces the problem of too less between connections so the contextual information learned by the former U-Net cannot be fully used by the latter one. In this article, we devise novel Inner Cascaded U-Net and Inner Cascaded U²-Net as improvements to plain cascaded U-Net for medical image segmentation. The proposed Inner Cascaded U-Net adds inner nested connections between two U-Nets to share more contextual information. To further boost segmentation performance, we propose Inner Cascaded U²-Net, which applies residual U-block to capture more global contextual information from different scales. The proposed models can be trained from scratch in an end-to-end fashion and have been evaluated on Multimodal Brain Tumor Segmentation Challenge (BraTS) 2013 and ISBI Liver Tumor Segmentation Challenge (LiTS) dataset in comparison to related U-Net, cascaded U-Net, U-Net++, U²-Net and state-of-the-art methods. Our experiments demonstrate that our proposed Inner Cascaded U-Net and Inner Cascaded U²-Net achieve better segmentation performance in terms of dice similarity coefficient and hausdorff distance as well as get finer outline segmentation.

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Keywords

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