无需设定阈值，实现医学图像异常区域精准分割
MIAS-SAM: Medical Image Anomaly Segmentation without thresholding
- 用记忆库存储正常图像特征，比对推理时的嵌入图块
- 在三个不同模态数据集上取得高DICE分数，无需手动调阈值
- 适合医疗影像异常检测场景，尤其适用于缺乏标注数据的领域
本文提出MIAS-SAM，一种无需设定阈值即可进行医学图像异常分割的新方法。该方法利用SAM编码器从正常数据中提取图像特征，并构建基于补丁的记忆库进行存储。推理时，将输入图像的嵌入补丁与记忆库中的特征进行对比，生成异常图。随后通过计算异常图的质心来引导SAM解码器，从而获得精确的分割结果。与以往方法不同，MIAS-SAM不依赖阈值选择即可完成分割。在三种公开数据集（脑部MRI、肝脏CT、视网膜OCT）上的实验表明，该方法在不同成像模态下均实现了高精度的异常分割，评估指标为DICE分数。代码已开源：https://github.com/warpcut/MIAS-SAM
原文摘要 · Abstract (English)
This paper presents MIAS-SAM, a novel approach for the segmentation of anomalous regions in medical images. MIAS-SAM uses a patch-based memory bank to store relevant image features, which are extracted from normal data using the SAM encoder. At inference time, the embedding patches extracted from the SAM encoder are compared with those in the memory bank to obtain the anomaly map. Finally, MIAS-SAM computes the center of gravity of the anomaly map to prompt the SAM decoder, obtaining an accurate segmentation from the previously extracted features. Differently from prior works, MIAS-SAM does not require to define a threshold value to obtain the segmentation from the anomaly map. Experimental results conducted on three publicly available datasets, each with a different imaging modality (Brain MRI, Liver CT, and Retina OCT) show accurate anomaly segmentation capabilities measured using DICE score. The code is available at: https://github.com/warpcut/MIAS-SAM
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