SynTaskNet: A synergistic multi-task network for joint segmentation and classification of small anatomical structures in ultrasound imaging

초록

Segmenting small, low-contrast anatomical structures and classifying their pathological status in ultrasound (US) images remain challenging tasks in computer vision, especially under the noise and ambiguity inherent in real-world clinical data. Papillary thyroid microcarcinoma (PTMC), characterized by nodules <= 1.0 cm, exemplifies these challenges where both precise segmentation and accurate lymph node metastasis (LNM) prediction are essential for informed clinical decisions. We propose SynTaskNet, a synergistic multi-task learning (MTL) architecture that jointly performs PTMC nodule segmentation and LNM classification from US images. Built upon a DenseNet201 backbone, SynTaskNet incorporates several specialized modules: a Coordinated Depth-wise Convolution (CDC) layer for enhancing spatial features, an Adaptive Context Block (ACB) for embedding contextual dependencies, and a Multi-scale Contextual Boundary Attention (MCBA) module to improve boundary localization in low-contrast regions. To strengthen task interaction, we introduce a Selective Enhancement Fusion (SEF) mechanism that hierarchically integrates features across three semantic levels, enabling effective information exchange between segmentation and classification branches. On top of this, we formulate a synergistic learning scheme wherein an Auxiliary Segmentation Map (ASM) generated by the segmentation decoder is injected into SEF's third class-specific fusion path to guide LNM classification. In parallel, the predicted LNM label is concatenated with the third-path SEF output to refine the Final Segmentation Map (FSM), enabling bidirectional task reinforcement. Extensive evaluations on a dedicated PTMC US dataset demonstrate that SynTaskNet achieves state-of-the-art performance, with a Dice score of 93.0% for segmentation and a classification accuracy of 94.2% for LNM prediction, validating its clinical relevance and technical efficacy.

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