UNet#: A UNet-like Redesigning Skip Connections for Medical Image Segmentation

• URL: http://arxiv.org/abs/2205.11759v1
• Date: Tue, 24 May 2022 03:40:48 GMT
• Title: UNet#: A UNet-like Redesigning Skip Connections for Medical Image Segmentation
• Authors: Ledan Qian, Xiao Zhou, Yi Li, Zhongyi Hu
• Abstract summary: We propose a novel network structure combining dense skip connections and full-scale skip connections, named UNet-sharp (UNet#) for its shape similar to symbol #. The proposed UNet# can aggregate feature maps of different scales in the decoder sub-network and capture fine-grained details and coarse-grained semantics from the full scale.
• Score: 13.767615201220138
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: As an essential prerequisite for developing a medical intelligent assistant system, medical image segmentation has received extensive research and concentration from the neural network community. A series of UNet-like networks with encoder-decoder architecture has achieved extraordinary success, in which UNet2+ and UNet3+ redesign skip connections, respectively proposing dense skip connection and full-scale skip connection and dramatically improving compared with UNet in medical image segmentation. However, UNet2+ lacks sufficient information explored from the full scale, which will affect the learning of organs' location and boundary. Although UNet3+ can obtain the full-scale aggregation feature map, owing to the small number of neurons in the structure, it does not satisfy the segmentation of tiny objects when the number of samples is small. This paper proposes a novel network structure combining dense skip connections and full-scale skip connections, named UNet-sharp (UNet\#) for its shape similar to symbol \#. The proposed UNet\# can aggregate feature maps of different scales in the decoder sub-network and capture fine-grained details and coarse-grained semantics from the full scale, which benefits learning the exact location and accurately segmenting the boundary of organs or lesions. We perform deep supervision for model pruning to speed up testing and make it possible for the model to run on mobile devices; furthermore, designing two classification-guided modules to reduce false positives achieves more accurate segmentation results. Various experiments of semantic segmentation and instance segmentation on different modalities (EM, CT, MRI) and dimensions (2D, 3D) datasets, including the nuclei, brain tumor, liver, and lung, demonstrate that the proposed method outperforms state-of-the-art models.

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