A benchmark bone marrow aspirate smear dataset and a multi-scale cell detection model for the diagnosis of hematological disorders. (June 2021)

• Record Type: Journal Article
• Authors: Su, Jie; Han, Jinjun; Song, Jinming
• Is Part Of: Computerized medical imaging and graphics. Volume 90(2021)
• Publication Date: 2021-06
• Subjects: Pathological analysis -- Bone marrow aspirate smear image -- Hematological diseases -- Detection model -- Deep learning; Diagnostic imaging -- Periodicals; Imaging systems in medicine -- Periodicals; Diagnosis, Radioscopic -- Data processing -- Periodicals; Diagnostic Imaging -- Periodicals; Imagerie pour le diagnostic -- Périodiques; Diagnostic imaging; Periodicals; Electronic journals; Electronic journals; 616.0754
• DOI: 10.1016/j.compmedimag.2021.101912
• Languages: English
• ISSNs: 0895-6111

Abstract

Abstract: Research on pathological diagnosis of hematopoietic disorders based on bone marrow aspirate smear images has attracted more and more attention with the development of deep learning methods. However, high quality bone marrow aspirate smear image datasets are not readily available because of the time, the efforts, and the medical knowledge required in the acquisition and manual annotation images. In order to facilitate the research of automated diagnosis of hematological disorders, we constructed a high quality Bone Marrow Aspirate Smear Image Dataset (BMASID), which contains 230 bone marrow aspirate smear images, all with the corresponding labeled images. We used additional clinical images as testing data, which are more challenging because of image noise, cell overlap, cell adhesion, blurred borders of cells and ambiguous types of cells. We also proposed a Cell Recognition Network (CRNet) that was trained on this benchmark dataset. CRNet is comprised of a cell detector to locate and recognize cells in the bone marrow aspirate images, and a cell classifier to classify the types of cells. New anchors and novel evaluation metrics are proposed and applied in CRNet. Benchmark evaluations of the proposed CRNet demonstrated the satisfactory performance of our state-of-the-art methods. Experimental results show that the detection precision by detector is more than 83%, and it is better when compared with other detection methods. After the cell type confirmation by the cell classifier, the precision is more than 95%. Compared with the most popular evaluation metrics Intersection over Union (IoU) and the newly proposed Generalized Intersection over Union (GIoU) used in the object detection benchmarks, our evaluation metrics are more suitable for the cell detection task with ambiguous cell boundaries. The proposed bone marrow aspirate smear image dataset and the proposed evaluation metrics can be used in the training and the evaluation of cell detection models, which contributes to future research in the pathological analysis and auxiliary diagnostic methods of hematological disorders. The codes are available at: https://github.com/SuJie-Med/hematolgical-disorders .