DC-UNet: rethinking the U-Net architecture with dual channel efficient CNN for medical image segmentation

Ange Lou; Shuyue Guan; Murray Loew

doi:10.1117/12.2582338

15 February 2021 DC-UNet: rethinking the U-Net architecture with dual channel efficient CNN for medical image segmentation

Ange Lou, Shuyue Guan, Murray Loew

Author Affiliations +

Proceedings Volume 11596, Medical Imaging 2021: Image Processing; 115962T (2021) https://doi.org/10.1117/12.2582338
Event: SPIE Medical Imaging, 2021, Online Only

Conference Poster

Abstract

Recently, deep learning has become much more popular in computer vision applications. The Convolutional Neural Network (CNN) has brought a breakthrough in image segmentation, especially for medical images. In this regard, the UNet is the predominant approach to the medical image segmentation task. The U-Net not only performs well in segmenting multimodal medical images generally, but also in some difficult cases. We found, however, that the classical U-Net architecture has limitations in several respects. Therefore, we applied modifications: 1) designed efficient CNN architecture to replace encoder and decoder, 2) applied residual module to replace skip connection between encoder and decoder to improve, based on the-state-of-the-art U-Net model. Following these modifications, we designed a novel architecture -- DC-UNet, as a potential successor to the U-Net architecture. We created a new effective CNN architecture and built the DC-UNet based on this CNN. We have evaluated our model on three datasets with difficult cases and have obtained a relative improvement in performance of 2.90%, 1.49%, and 11.42% respectively compared with classical UNet. In addition, we used the Tanimoto similarity measure to replace the Jaccard measure for gray-to-gray image comparisons.

Conference Presentation

Citation Download Citation

Ange Lou, Shuyue Guan, and Murray Loew "DC-UNet: rethinking the U-Net architecture with dual channel efficient CNN for medical image segmentation", Proc. SPIE 11596, Medical Imaging 2021: Image Processing, 115962T (15 February 2021); https://doi.org/10.1117/12.2582338

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