Liu J, Yuan R, Li Y, Zhou L, Zhang Z, Yang J, Xiao L. A deep learning method and device for bone marrow imaging cell detection.
ANNALS OF TRANSLATIONAL MEDICINE 2022;
10:208. [PMID:
35280370 PMCID:
PMC8908139 DOI:
10.21037/atm-22-486]
[Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/05/2022] [Accepted: 02/18/2022] [Indexed: 11/06/2022]
Abstract
BACKGROUND
Morphological analysis of bone marrow cells is considered as the gold standard for the diagnosis of leukemia. However, due to the diverse morphology of bone marrow cells, extensive experience and patience are needed for morphological examination. automatic diagnosis system through the comprehensive application of image analysis and pattern recognition technology is urgently needed to reduce work intensity, error probability and improves work efficiency.
METHODS
In this article, we establish a new morphological diagnosis system for bone marrow cell detection based on the deep learning object detection framework. The model is based on the Faster Region-Convolutional Neural Network (R-CNN), a classical object detection model. The system automatically detects bone marrow cells and determines their types. As specimens have severe long-tail distribution, i.e., the frequency of different types of cells varies dramatically, we proposed a general score ranking loss to solve such a problem. The general score ranking loss considers the ranking relationship between positive and negative samples and optimizes the positive sample with a higher classification probability value.
RESULTS
We verified this system with 70 bone marrow specimens of leukemia patients, which proved that it can realize intelligent recognition with high efficiency. The software is finally integrated into the microscope system to build an augmented reality system.
CONCLUSIONS
Clinical tests show that the response speed of the newly developed diagnostic system is faster than that of trained diagnostic experts.
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