Related Experiment Video
Updated: Apr 1, 2026
09:31
Automated Quantification of Hematopoietic Cell – Stromal Cell Interactions in Histological Images of Undecalcified Bone
Published on: April 8, 2015
12.1K
Automatic recognition of myeloma cells in microscopic images using bottleneck algorithm, modified watershed and SVM
Z Saeedizadeh1, A Mehri Dehnavi1,2, A Talebi3
1Department of Biomedical Engineering, Faculty of Advanced Medical Technologies, Isfahan University of Medical Sciences, Isfahan, Iran.
Journal of Microscopy
|October 13, 2015
Summary
This study developed a computer-aided diagnostic method to identify myeloma cells in bone marrow smears, reducing diagnostic time and improving accuracy for pathologists. The automated system achieved high sensitivity and specificity in classifying normal plasma cells and cancerous myeloma cells.
Area of Science:
- Hematology
- Computational Pathology
- Medical Diagnostics
Background:
- Plasma cells, derived from B lymphocytes, produce antibodies crucial for fighting infections.
- Multiple myeloma is a bone marrow cancer characterized by abnormal plasma cells (myeloma cells).
- Current diagnosis relies on manual microscopic examination of bone marrow smears, which is subjective, time-consuming, and prone to errors.
Purpose of the Study:
- To develop and evaluate a computer-aided diagnostic method for accurate and efficient identification of myeloma cells in bone marrow smears.
- To reduce diagnostic time and subjectivity associated with manual pathological review.
- To provide a potential second opinion tool for pathologists in multiple myeloma diagnosis.
Main Methods:
- A computer-aided diagnostic approach was developed for analyzing digital bone marrow smear images.
- The method involved separating white blood cells from red blood cells and background.
- Plasma cells were detected using feature extraction and decision rules, followed by classification of normal plasma cells versus myeloma cells.
Main Results:
- The algorithm was tested on 50 digital images containing 678 cells (132 normal plasma cells, 256 myeloma cells, 290 other marrow cells).
- The computer-aided diagnostic method demonstrated high performance metrics.
- Achieved sensitivity of 96.52%, specificity of 93.04%, and precision of 95.28% in identifying myeloma cells.
Conclusions:
- The developed computer-aided diagnostic method shows significant potential for accurate and efficient myeloma cell detection.
- This automated approach can assist pathologists, reduce diagnostic errors, and expedite the diagnosis of multiple myeloma.
- Further validation on larger datasets is recommended to solidify its clinical utility.
