FAMSeg: Fetal Femur and Cranial Ultrasound Segmentation Using Feature-Aware Attention and Mamba Enhancement
Journal:
arXiv
Published Date:
Jun 9, 2025
Abstract
Accurate ultrasound image segmentation is a prerequisite for precise
biometrics and accurate assessment. Relying on manual delineation introduces
significant errors and is time-consuming. However, existing segmentation models
are designed based on objects in natural scenes, making them difficult to adapt
to ultrasound objects with high noise and high similarity. This is particularly
evident in small object segmentation, where a pronounced jagged effect occurs.
Therefore, this paper proposes a fetal femur and cranial ultrasound image
segmentation model based on feature perception and Mamba enhancement to address
these challenges. Specifically, a longitudinal and transverse independent
viewpoint scanning convolution block and a feature perception module were
designed to enhance the ability to capture local detail information and improve
the fusion of contextual information. Combined with the Mamba-optimized
residual structure, this design suppresses the interference of raw noise and
enhances local multi-dimensional scanning. The system builds global information
and local feature dependencies, and is trained with a combination of different
optimizers to achieve the optimal solution. After extensive experimental
validation, the FAMSeg network achieved the fastest loss reduction and the best
segmentation performance across images of varying sizes and orientations.
