DoubleU-Net: A Deep Convolutional Neural Network for Medical Image Segmentation

• URL: http://arxiv.org/abs/2006.04868v2
• Date: Sat, 27 Jun 2020 15:40:40 GMT
• Title: DoubleU-Net: A Deep Convolutional Neural Network for Medical Image Segmentation
• Authors: Debesh Jha, Michael A. Riegler, Dag Johansen, P{\aa}l Halvorsen, H{\aa}vard D. Johansen
• Abstract summary: DoubleU-Net is a combination of two U-Net architectures stacked on top of each other. We have evaluated DoubleU-Net using four medical segmentation datasets.
• Score: 1.6416058750198184
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Semantic image segmentation is the process of labeling each pixel of an image with its corresponding class. An encoder-decoder based approach, like U-Net and its variants, is a popular strategy for solving medical image segmentation tasks. To improve the performance of U-Net on various segmentation tasks, we propose a novel architecture called DoubleU-Net, which is a combination of two U-Net architectures stacked on top of each other. The first U-Net uses a pre-trained VGG-19 as the encoder, which has already learned features from ImageNet and can be transferred to another task easily. To capture more semantic information efficiently, we added another U-Net at the bottom. We also adopt Atrous Spatial Pyramid Pooling (ASPP) to capture contextual information within the network. We have evaluated DoubleU-Net using four medical segmentation datasets, covering various imaging modalities such as colonoscopy, dermoscopy, and microscopy. Experiments on the MICCAI 2015 segmentation challenge, the CVC-ClinicDB, the 2018 Data Science Bowl challenge, and the Lesion boundary segmentation datasets demonstrate that the DoubleU-Net outperforms U-Net and the baseline models. Moreover, DoubleU-Net produces more accurate segmentation masks, especially in the case of the CVC-ClinicDB and MICCAI 2015 segmentation challenge datasets, which have challenging images such as smaller and flat polyps. These results show the improvement over the existing U-Net model. The encouraging results, produced on various medical image segmentation datasets, show that DoubleU-Net can be used as a strong baseline for both medical image segmentation and cross-dataset evaluation testing to measure the generalizability of Deep Learning (DL) models.

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