3. Digital cytology analysis

Statistical analysis of cells and cellular features can guide a pathologist's diagnosis of leukemia. The number of RBCs, WBCs, and platelets, the proportion of specific immature and mature cells, and more detailed morphological features recognized by automated WSI can all help direct a diagnosis. Digital image analysis of BM biopsy has been applied to study relapse in AML [17, 18]. The focus of most prior studies has been recognition of the acute leukemia FAB subtypes or differentiation of acute and chronic leukemia from the PB and BM smears. While PB and BM smears can differ in the type and maturation of their cells, the quantitative process to recognize the cell types and extract morphometric information is similar. We describe it below:

Following steps a pathologist would take, computer-based digital pathology aims to detect, localize, and recognize the specific cell type under study (Figure 2). An acquired image is preprocessed through image enhancement steps; then, cells are detected, and cell boundaries are traced out using segmentation algorithms for morphometric analysis.

Processing steps to automate the analysis of leukemia smear images are shown in Figure 2. A whole slide image (WSI) of a Wright-Giemsa-stained bone marrow smear is shown in Figure 2a. This is a typical smear image annotated by a pathologist. The Wright-Giemsastained image reveals RBCs as smaller pink cells without nuclei (either distributed as single cells or clustered) and a few WBCs of different sizes with dark purple nuclei. Notably, image acquisition techniques, staining methods, and digitization protocols can differ in each laboratory. Furthermore, environmental effects can introduce artifacts and degrade the quality of the image. Image preprocessing steps can improve the image quality and correct for differences in protocols and in illumination. Methods of image correction techniques include illumination

Figure 2. Image processing pipeline (a–e: Image from Carlos Bueso-Ramos, MD, PhD, MD Anderson Cancer Center).

normalization, color or stain correction for image enhancement, contrast enhancement and smoothing, contrast stretching, and histogram equalization [19–21].

Leukemia is detected based on the number, type, and proportion of various cell types in the blood. Segmentation algorithms enable identification of individual cells from smear images (Figure 2b); these algorithms can distinguish overlapping cells from individual cells in order to extract cell-based features and can also divide each WBC into its components: cell membrane, nucleus, and cytoplasm (Figure 2c) [19, 21–37]. Following segmentation, metrics can be extracted from the WBCs and their subcellular components (cell size, nuclear size, etc.).
