LHU-Net: A Light Hybrid U-Net for Cost-Efficient, High-Performance Volumetric Medical Image Segmentation

As a result of the rise of Transformer architectures in medical image analysis, specifically in the domain of medical image segmentation, a multitude of hybrid models have been created that merge the advantages of Convolutional Neural Networks (CNNs) and Transformers. These hybrid models have achieved notable success by significantly improving segmentation accuracy. Yet, this progress often comes at the cost of increased model complexity, both in terms of parameters and computational demand. Moreover, many of these models fail to consider the crucial interplay between spatial and channel features, which could further refine and improve segmentation outcomes. To address this, we introduce LHU-Net, a Light Hybrid U-Net architecture optimized for volumetric medical image segmentation. LHU-Net is meticulously designed to prioritize spatial feature analysis in its initial layers before shifting focus to channel-based features in its deeper layers, ensuring a comprehensive feature extraction process. Rigorous evaluation across five benchmark datasets - Synapse, LA, Pancreas, ACDC, and BRaTS 2018 - underscores LHU-Net's superior performance, showcasing its dual capacity for efficiency and accuracy. Notably, LHU-Net sets new performance benchmarks, such as attaining a Dice score of 92.66 on the ACDC dataset, while simultaneously reducing parameters by 85% and quartering the computational load compared to existing state-of-the-art models. Achieved without any reliance on pre-training, additional data, or model ensemble, LHU-Net's effectiveness is further evidenced by its state-of-the-art performance across all evaluated datasets, utilizing fewer than 11 million parameters. This achievement highlights that balancing computational efficiency with high accuracy in medical image segmentation is feasible. Our implementation of LHU-Net is freely accessible to the research community on GitHub.

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

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}