RTCMUNet: retinex-inspired feature modulation for breast ultrasound lesion segmentation.

Objective.Breast ultrasound lesion segmentation remains challenging because heterogeneous echogenicity, speckle, posterior shadowing, and weak boundaries impair lesion-margin delineation.Approach.We evaluate a CMU-Net-based implementation, termed RTCMUNet, in which the proposed ML-Retinex block modulates skip features before the inherited CMU-Net multi-scale attention gate. ML-Retinex combines learnable multi-scale projections, fixed Gaussian low-pass context estimates, clipped feature-ratio cues, and residual gating. It operates on learned skip features and neither estimates nor corrects scanner time-gain compensation. Nine models were evaluated on BUSI under a common three-run internal-validation protocol whose mutually disjoint validation subsets formed a pooled set of 389 cases. We assessed seven case-level metrics, paired Wilcoxon tests against CMU-Net with Holm correction, exploratory design-stage analyses, diagnostic controls, a secondary historical BUS/BUSI/TNSCUI benchmark, and a synthetic brightness-gradient stress proxy.Main results.RTCMUNet achieved Dice, IoU, HD95 px, and ASSD px. Sensitivity was, precision, and BF1. Relative to CMU-Net, Dice and IoU increased byand, while HD95 and ASSD decreased byand px. All seven paired tests remained significant after Holm correction (largest adjusted). RTCMUNet addedparameters and maintained 42 frames per second.Significance.Within retrospective internal validation, and without scanner-level gain data or an independent test set, the results support further evaluation of ML-Retinex as a compact feature-modulation strategy in the tested CMU-Net/BUSI setting.