AEPL: Automated and Editable Prompt Learning for Brain Tumor Segmentation

Brain tumor segmentation is crucial for accurate diagnosis and treatment planning, but the small sizes and irregular shapes of tumors pose significant challenges. Existing methods often fail to effectively incorporate medical domain knowledge such as tumor grade, which correlates with tumor aggressiveness and morphology, providing critical insights for more accurate detection of tumor subregions during segmentation. We propose an Automated and Editable Prompt Learning (AEPL) framework that integrates tumor grade into the segmentation process by combining multi-task learning and prompt learning with automatic and editable prompt generation. Specifically, AEPL employs an encoder to extract image features for both tumor-grade prediction and segmentation mask generation. The predicted tumor grades serve as auto-generated prompts, guiding the decoder to produce precise segmentation masks. This eliminates the need for manual prompts while allowing clinicians to manually edit the auto-generated prompts to fine-tune the segmentation, enhancing both flexibility and precision. The proposed AEPL achieves state-of-the-art performance on the BraTS 2018 dataset, demonstrating its effectiveness and clinical potential. The source code can be accessed online.