A Multi-Scale Interpretability-Based PET-CT Tumor Segmentation Method

Accurate tumor segmentation is crucial for clinical diagnosis, treatment planning, and efficacy evaluation in medical imaging. Although traditional image processing techniques have been widely applied in tumor segmentation, they often perform poorly when dealing with tumors that have low contrast, irregular shapes, or varying sizes. With the rise of deep learning, particularly the application of convolutional neural networks (CNNs) in medical image segmentation, significant progress has been made, especially in handling multimodal data such as positron emission tomography (PET) and computed tomography (CT). However, the “black-box” nature of CNNs presents challenges for interpretability, which is particularly important in clinical applications. To address this, we propose a tumor segmentation framework based on a multi-scale interpretability module (MSIM). Through ablation experiments and comparisons on three public datasets, we evaluate the performance of the proposed method. The ablation results show that the proposed method achieves an improvement of 1.6, 1.62, and 2.36 in the Dice Similarity Coefficient (DSC) on the Melanoma, Lymphoma, and Lung Cancer datasets, respectively, highlighting the benefits of the interpretability module. Furthermore, the method outperforms the best comparative methods on all three datasets, achieving DSC improvements of 1.46, 1.27, and 1.93, respectively. Finally, visualization and perturbation experiments further validate the effectiveness of our method in emphasizing critical features.