A case of B-cell lymphoma, unclassifiable, with features intermediate between diffuse large B-cell lymphoma and Burkitt lymphoma in a Korean child

A segmentation method based on HMRF for the aided diagnosis of acute myeloid leukemia

BACKGROUND AND OBJECTIVES

The diagnosis of acute myeloid leukemia (AML) is purely dependent on counting the percentages of blasts (>20%) in the peripheral blood or bone marrow. Manual microscopic examination of peripheral blood or bone marrow aspirate smears is time consuming and less accurate. The first and very important step in blast recognition is the segmentation of the cells from the background for further cell feature extraction and cell classification. In this paper, we aimed to utilize computer technologies in image analysis and artificial intelligence to develop an automatic program for blast recognition and counting in the aspirate smears.

METHODS

We proposed a method to analyze the aspirate smear images, which first performs segmentation of the cells by k-means cluster, then builds cell image representing model by HMRF (Hidden-Markov Random Field), estimates model parameters through probability of EM (expectation maximization), carries out convergence iteration until optimal value, and finally achieves second stage refined segmentation. Furthermore, the segmentation results are compared with several other methods using six classes of cells respectively.

RESULTS

The proposed method was applied to six groups of cells from 61 bone marrow aspirate images, and compared with other algorithms for its performance on the analysis of the whole images, the segmentation of nucleus, and the efficiency of calculation. It showed improved segmentation results in both the cropped images and the whole images, which provide the base for down-stream cell feature extraction and identification.

CONCLUSIONS

Segmentation of the aspirate smear images using the proposed method helps the analyst in differentiating six groups of cells and in the determination of blasts counting, which will be of great significance for the diagnosis of acute myeloid leukemia.

Clinicopathological Analysis of B Cell Lymphomas, Unclassifiable; with Features Intermediate Between Diffuse Large B-Cell Lymphoma and Burkitt Lymphoma in a Tertiary Care Hospital in Southern India

B-cell lymphomas, unclassifiable; with features intermediate between large B-cell lymphoma and Burkitt lymphoma (BCLu-DLBCL/BL) is a new entity included in the recent World Health Organization (WHO) classification of Tumours of the Hematopoietic and Lymphoid Tissues (2008) to overcome the problems of difficulty in classifying certain lymphomas having overlapping morphological, immunophenotypical and genetic features. To study the clinicopathological profile of BCLu-DLBCL/BL. Cross-sectional study over 3 year period in the Haematology section of Department of Pathology in a large teaching hospital in Southern India from January 2011 to December 2013. All the cases reported as BCLu-DLBCL/BL were collected and the clinical, morphological and immunohistochemical parameters were analyzed. Descriptive statistics. There were seven cases, four males and three females, of age ranging from 20 to 70 years. Five cases had extranodal involvement. Four cases had Burkitt morphology with strong Bcl2 positivity and absent CD10 expression. One case had the morphology and immunophenotype that of typical BL, along with strong positivity to Bcl2 suggesting a double hit hypothesis. Two cases had morphology and immunophenotype of BL with low Ki 67. Three patients on follow up had adverse outcome. BCLu-DLBCL/BL, a provisional category in WHO 2008 is useful in classifying the cases not meeting the criteria for classical BL or DLBCL. Each of these cases was interesting with different sites of involvement, different morphological features and immunophenotype with most of the patients on follow up ending with a grave prognosis.