DermoSegDiff: A Boundary-aware Segmentation Diffusion Model for Skin Lesion Delineation

arXiv (Cornell University)

Abstract

Skin lesion segmentation plays a critical role in the early detection and accurate diagnosis of dermatological conditions. Denoising Diffusion Probabilistic Models (DDPMs) have recently gained attention for their exceptional image-generation capabilities. Building on these advancements, we propose DermoSegDiff, a novel framework for skin lesion segmentation that incorporates boundary information during the learning process. Our approach introduces a novel loss function that prioritizes the boundaries during training, gradually reducing the significance of other regions. We also introduce a novel U-Net-based denoising network that proficiently integrates noise and semantic information inside the network. Experimental results on multiple skin segmentation datasets demonstrate the superiority of DermoSegDiff over existing CNN, transformer, and diffusion-based approaches, showcasing its effectiveness and generalization in various scenarios. The implementation is publicly accessible on \href{https://github.com/mindflow-institue/dermosegdiff}{GitHub}

Authors 5

1. Afshin Bozorgpour

   University of Regensburg

   Affiliation as printed

   Institute of Image Analysis and Computer Vision , Faculty of Informatics and Data Science , University of Regensburg , Regensburg , Germany

2. Yousef Sadegheih

   University of Regensburg

   Affiliation as printed

   Institute of Image Analysis and Computer Vision , Faculty of Informatics and Data Science , University of Regensburg , Regensburg , Germany

3. Amirhossein Kazerouni

   Iran University of Science and Technology

   Affiliation as printed

   School of Electrical Engineering , Iran University of Science and Technology , Iran

4. Reza Azad Aachen Faculty of Electrical Engineering and Information Technology

   RWTH Aachen University

   Affiliation as printed

   Faculty of Electrical Engineering and Information Technology , RWTH Aachen University , Aachen , Germany

5. Dorit Merhof

   Fraunhofer Institute for Digital Medicine · University of Regensburg

   Affiliation as printed

   Fraunhofer Institute for Digital Medicine MEVIS , Bremen , Germany

   Institute of Image Analysis and Computer Vision , Faculty of Informatics and Data Science , University of Regensburg , Regensburg , Germany