AEPL: Automated and Editable Prompt Learning for Brain Tumor Segmentation

• URL: http://arxiv.org/abs/2410.19847v1
• Date: Mon, 21 Oct 2024 20:29:44 GMT
• Title: AEPL: Automated and Editable Prompt Learning for Brain Tumor Segmentation
• Authors: Yongheng Sun, Mingxia Liu, Chunfeng Lian,
• Abstract summary: Brain tumor segmentation is crucial for accurate diagnosis and treatment planning. Existing methods of-ten fail to effectively incorporate medical domain knowledgesuch as tumor grade. We propose an Automated and Editable Prompt Learning framework that integrates tumor grade into the seg-mentation process.
• Score: 12.347340694969212
• License:
• Abstract: Brain tumor segmentation is crucial for accurate diagnosisand treatment planning, but the small size and irregular shapeof tumors pose significant challenges. Existing methods of-ten fail to effectively incorporate medical domain knowledgesuch as tumor grade, which correlates with tumor aggres-siveness and morphology, providing critical insights for moreaccurate detection of tumor subregions during segmentation.We propose an Automated and Editable Prompt Learning(AEPL) framework that integrates tumor grade into the seg-mentation process by combining multi-task learning andprompt learning with automatic and editable prompt gen-eration. Specifically, AEPL employs an encoder to extractimage features for both tumor-grade prediction and segmen-tation mask generation. The predicted tumor grades serveas auto-generated prompts, guiding the decoder to produceprecise segmentation masks. This eliminates the need formanual prompts while allowing clinicians to manually editthe auto-generated prompts to fine-tune the segmentation,enhancing both flexibility and precision. The proposed AEPLachieves state-of-the-art performance on the BraTS 2018dataset, demonstrating its effectiveness and clinical potential.The source code can be accessed online.

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