SmoothSegNet: A global-local framework for liver tumor segmentation with clinical knowledge-informed label smoothing
Hairong Wang, Lingchao Mao, Zihan Zhang, Jing Li · IISE Transactions on Healthcare Systems Engineering · 2025
Liver cancer is a leading cause of mortality worldwide, and accurate computed tomography (CT)-based tumor segmentation is essential for diagnosis and treatment. Manual delineation is time-intensive, prone to variability, and highlights the need for reliable automation. While deep learning has shown promise for automated liver segmentation, precise liver tumor segmentation remains challenging. We present SmoothSegNet, a novel deep learning framework that addresses this challenge with the three key designs: (1) a novel knowledge-informed label smoothing technique that distills knowledge from clinical data to generate smooth labels, which are used to regularize model training, reducing the overfitting risk and enhancing model performance; (2) a global and local segmentation framework that breaks down the main task into two simpler subtasks, allowing optimized preprocessing and training for each; and (3) pre- and post-processing pipelines customized to the challenges of each subtask aimed to enhance tumor visibility and refines tumor boundaries. We apply the proposed model on a challenging HCC-TACE-Seg dataset and show that SmoothSegNet outperformed various benchmarks in segmentation performance, particularly at smaller tumors (<10 cm). Our ablation studies show that the three design components complementarily contribute to the model improved performance. Code for the proposed method are available at https://github.com/lingchm/medassist-liver-cancer.