MCBL-UNet: A Hybrid Mamba-CNN Boundary Enhanced Light-weight UNet for Placenta Ultrasound Image Segmentation.

Abstract

The shape and size of the placenta are closely related to fetal development in the second and third trimesters of pregnancy. Accurately segmenting the placental contour in ultrasound images is a challenge because it is limited by image noise, fuzzy boundaries, and tight clinical resources. To address these issues, we propose MCBL-UNet, a novel lightweight segmentation framework that combines the long-range modeling capabilities of Mamba and the local feature extraction advantages of convolutional neural networks (CNNs) to achieve efficient segmentation through multi-information fusion. Based on a compact 6-layer U-Net architecture, MCBL-UNet introduces several key modules: a boundary enhancement module (BEM) to extract fine-grained edge and texture features; a multi-dimensional global context module (MGCM) to capture global semantics and edge information in the deep stages of the encoder and decoder; and a parallel channel spatial attention module (PCSAM) to suppress redundant information in skip connections while enhancing spatial and channel correlations. To further improve feature reconstruction and edge preservation capabilities, we introduce an attention downsampling module (ADM) and a content-aware upsampling module (CUM). MCBL-UNet has achieved excellent segmentation performance on multiple medical ultrasound datasets (placenta, gestational sac, thyroid nodules). Using only 1.31M parameters and 1.26G FLOPs, the model outperforms 13 existing mainstream methods in key indicators such as Dice coefficient and mIoU, showing a perfect balance between high accuracy and low computational cost. This model is not only suitable for resource-constrained clinical environments, but also provides a new idea for introducing the Mamba structure into medical image segmentation.