Transfer Learning with Interpretability: Liver Segmentation in CT and MR using Limited Dataset

Liver segmentation using deep learning (DL) is a widely discussed topic and this applies for both the modalities Computed Tomography (CT) and Magnetic Resonance Imaging (MR). However, the development of these DL networks often encounters a major hurdle: the need for large amounts of data. In this paper, we present a general solution to this challenge, by leveraging transfer learning with a conventional UNet architecture for CT and MR liver parenchyma segmentation. Our method capitalizes on publicly available data from one modality to train the model, thereby learning the features of the specific task. Limited cases from the target domain are then used for further training. By comparing with the performance of 2D diffusion model for parenchyma segmentation tasks, we propose that this approach effectively avoids the need for resource-intensive models in scenarios where time and data resources are constrained. Moreover, to ensure transparency in our model's predictions, we have incorporated interpretability of the predictions using Explainable Artificial Intelligence (XAI). This allows for meaningful visual explanations of the segmentation outputs, fostering trust in the model's decisions. Our approach demonstrates good performance on both modalities with a mean test Dice of 90.01% in CT and 79.05% in MR, emphasizing the effectiveness of transfer learning in the development of a pre-trained CT and MR liver segmentation model with XAI.