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


🔘 – Principal

🔘 Paper page:


“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 “.


undefinedDebesh Jha. PhD Research Fellow at Simula Research Lab and UIT, Applied Mach. Lear. & Deep Learn., Computer Vision, Medical Multimedia, AI in healthcare researcher

undefinedMichael A. Riegler. Chief Research Scientist

Dag Johansen. Directing the inter-disciplinary Corpore Sano Centre, a centre at the intersection of computer science, medicine, health, sport science, and nutritional science.

Pål Halvorsen. (PhD) degree in computer science from Department of Informatics, University of Oslo

undefined Håvard Dagenborg Johansen. University of Tromsø UIT The Arctic University of Norway

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