用核周环替代细胞膜分割，提升白血病细胞识别准确率
PRISM: Perinuclear Ring-based Image Segmentation Method for Acute Lymphoblastic Leukemia Classification
- 以核为中心构建同心区域，避免依赖精确边界检测
- 提取颜色与纹理特征，实现98.46%准确率和0.9937 AUC
- 适合染色差异大、图像质量不一的临床病理分析
急性淋巴细胞白血病（ALL）外周血涂片的自动化分析受限于对比度低和胞质形态差异大，传统基于膜的分割方法难以应对。现有方法多依赖复杂神经网络和大量训练数据，仍难跨染色与采集条件泛化。为此，我们提出核周环图像分割方法（PRISM），摒弃显式胞质轮廓提取，转而围绕细胞核构建自适应同心区域。该区域融合颜色信息与灰度共生矩阵纹理统计量，生成鲁棒胞质特征，无需精确细胞边界。采用校准堆叠集成的传统分类器，基于这些特征实现高精度分类，准确率达98.46%，精确率-召回率AUC为0.9937。
原文摘要 · Abstract (English)
Automated analysis of peripheral blood smears for Acute Lymphoblastic Leukemia (ALL) is hindered by low contrast and substantial variability in cytoplasmic appearance, which complicate conventional membrane-based segmentation. We found that many recent approaches rely on heavy neural architectures and extensive training, but still struggle to generalize across staining and acquisition variability. To address these limitations, we propose the Perinuclear Ring-based Image Segmentation Method (PRISM), which replaces explicit cytoplasmic delineation with adaptive concentric zones constructed around the nucleus. These perinuclear regions enable the extraction of robust cytoplasmic descriptors by integrating color information with texture statistics derived from grey-level co-occurrence patterns, without requiring accurate cell-boundary detection. A calibrated stacking ensemble of traditional classifiers leverages these descriptors to achieve a high performance, with an accuracy of 98.46% and a precision-recall AUC of 0.9937.
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