Bridging the quality gap: Robust colon wall segmentation in noisy transabdominal ultrasound

Abstract

Colon wall segmentation in transabdominal ultrasound is challenging due to variations in image quality, speckle noise, and ambiguous boundaries. Existing methods struggle with low-quality images due to their inability to adapt to varying noise levels, poor boundary definition, and reduced contrast in ultrasound imaging, resulting in inconsistent segmentation performance. We present a novel quality-aware segmentation framework that simultaneously predicts image quality and adapts the segmentation process accordingly. Our approach uses a U-Net architecture with a ConvNeXt encoder backbone, enhanced with a parallel quality prediction branch that serves as a regularization mechanism. Our model learns robust features by explicitly modeling image quality during training. We evaluate our method on the C-TRUS dataset and demonstrate superior performance compared to state-of-the-art approaches, particularly on challenging low-quality images. Our method achieves Dice scores of 0.7780, 0.7025, and 0.5970 for high, medium, and low-quality images, respectively. The proposed quality-aware segmentation framework represents a significant step toward clinically viable automated colon wall segmentation systems.