将肿瘤分级自动融入分割，提升脑瘤识别精度。
AEPL: Automated and Editable Prompt Learning for Brain Tumor Segmentation
- 用多任务学习自动生成肿瘤分级提示，指导分割
- 在BraTS 2018上达到当前最优性能
- 支持医生手动编辑提示，兼顾精度与临床灵活性
脑瘤分割对精准诊断和治疗规划至关重要，但肿瘤体积小、形状不规则，给分割带来挑战。现有方法常无法有效融合如肿瘤分级等医学领域知识，而肿瘤分级与肿瘤侵袭性及形态相关，对细分区域检测具有关键意义。我们提出自动化可编辑提示学习框架（AEPL），通过结合多任务学习与提示学习，实现肿瘤分级信息的自动融入。该框架使用编码器同时提取图像特征以预测肿瘤分级并生成分割掩码，预测的分级结果作为自动生成的提示，引导解码器生成精确分割结果。该方法无需人工设定提示，同时允许临床医生手动修改自动生成的提示，以微调分割效果，提升灵活性与精度。AEPL在BraTS 2018数据集上取得当前最优表现，验证了其有效性与临床应用潜力。源代码已公开。
原文摘要 · Abstract (English)
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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