<br>核内马赛克模式（InMop）和全细胞 Haralick 组合描述符，用于识别和表征单细胞外周血图像上的急性白血病原始细胞,Cytometry Part A

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核内马赛克模式（InMop）和全细胞 Haralick 组合描述符，用于识别和表征单细胞外周血图像上的急性白血病原始细胞
Cytometry Part A ( IF 2.5 ) Pub Date : 2023-08-11 , DOI: 10.1002/cyto.a.24785
Jonathan Tarquino ₁ , Sara Arabyarmohammadi ₂ , Rafael Enrique Tejada ₃ , Anant Madabhushi _{2,

4} , Eduardo Romero ₁

Affiliation

急性白血病通常在外周血检测显示至少 20% 的异常未成熟细胞（原始细胞）时被诊断为急性白血病，在复发性细胞遗传学异常的情况下，这个数字甚至更低。原始细胞鉴定对于白细胞（WBC）计数至关重要，白细胞计数取决于识别细胞类型和表征细胞形态，这些过程容易受到观察者之间和观察者内部差异的影响。本研究引入了一种图像组合描述符来检测爆炸并确定其可能的谱系。该策略使用核内马赛克模式（InMop）描述符来捕获 WBC 内细微的细胞核差异，并使用 Haralick 的统计数据来量化细胞核和细胞质的局部结构。InMop 通过在自动分割的细胞核的重复图案（马赛克）上应用多尺度剪切力分解来捕获 WBC 内核结构。作为补充，Haralick 的统计量从强度共生矩阵表示中表征了整个细胞的局部结构。InMoP 和基于 Haralick 的描述符都是使用 Lab 颜色空间中的 b 通道计算的。通过在两个公共和独立的数据库中使用支持向量机（SVM）分类器和不同的转化内核区分原始细胞和非白血病细胞来评估组合描述符。第一个数据库 D1 （n = 260）由健康和急性淋巴细胞白血病（ALL）单细胞图像组成，第二个数据库 D2 包含急性髓性白血病（AML）原始细胞（n = 3294）和非原始细胞（n = 15,071）细胞图像。在第一个实验中，通过使用 D2 的子集（n = 6588）进行训练并在 D1 （n = 260）中进行测试来执行原始细胞与非原始细胞的区分，获得 0.991 ± 0.002 的训练 AUC 和 AUC = 0.782 用于独立验证。第二个实验自动区分 AML 原始细胞（来自 D2 的 260 张图像）和 ALL 原始细胞（来自 D1 的 260 张图像），AUC 为 0.93。在第三个实验中，最先进的策略 VGG16 和 RESNEXT 卷积神经网络（CNN）在两个数据库中将原始细胞与非原始细胞分开。VGG16 的 AUC 为 0.673，RESNEXT 为 0.75。所有实验的报告指标均为 ROC 曲线下面积（AUC）、准确度和 F1 分数。

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Intra-nucleus mosaic pattern (InMop) and whole-cell Haralick combined-descriptor for identifying and characterizing acute leukemia blasts on single cell peripheral blood images

Acute leukemia is usually diagnosed when a test of peripheral blood shows at least 20% of abnormal immature cells (blasts), a figure even lower in case of recurrent cytogenetic abnormalities. Blast identification is crucial for white blood cell (WBC) counting, which depends on both identifying the cell type and characterizing the cellular morphology, processes susceptible of inter- and intraobserver variability. The present work introduces an image combined-descriptor to detect blasts and determine their probable lineage. This strategy uses an intra-nucleus mosaic pattern (InMop) descriptor that captures subtle nuclei differences within WBCs, and Haralick's statistics which quantify the local structure of both nucleus and cytoplasm. The InMop captures WBC inner-nucleus structure by applying a multiscale Shearlet decomposition over a repetitive pattern (mosaic) of automatically-segmented nuclei. As a complement, Haralick's statistics characterize the local structure of the whole cell from an intensity co-occurrence matrix representation. Both InMoP and Haralick-based descriptors are calculated using the b-channel from Lab color-space. The combined-descriptor is assessed by differentiating blasts from nonleukemic cells with support vector machine (SVM) classifiers and different transformation kernels, in two public and independent databases. The first database-D1 (n = 260) is composed of healthy and acute lymphoid leukemia (ALL) single cell images, and second database-D2 contains acute myeloid leukemia (AML) blasts (n = 3294) and nonblast (n = 15,071) cell images. In a first experiment, blasts versus nonblast differentiation is performed by training with a subset of D2 (n = 6588) and testing in D1 (n = 260), obtaining a training AUC of 0.991 ± 0.002 and AUC = 0.782 for the independent validation. A second experiment automatically differentiates AML blasts (260 images from D2) from ALL blasts (260 images from D1), with an AUC of 0.93. In a third experiment, state-of-the-art strategies, VGG16 and RESNEXT convolutional neural networks (CNN), separate blast from nonblast cells in both databases. The VGG16 showed an AUC of 0.673 and the RESNEXT of 0.75. Reported metrics for all the experiments are area under the ROC curve (AUC), accuracy and F1-score.

更新日期：2023-08-11

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