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Updated: Apr 1, 2026
Automated Quantification of Hematopoietic Cell – Stromal Cell Interactions in Histological Images of Undecalcified Bone
Published on: April 8, 2015
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.
Abstract:
Plasma cells are developed from B lymphocytes, a type of white blood cells that is generated in the bone marrow. The plasma cells produce antibodies to fight with bacteria and viruses and stop infection and disease. Multiple myeloma is a cancer of plasma cells that collections of abnormal plasma cells (myeloma cells) accumulate in the bone marrow. The definitive diagnosis of multiple myeloma is done by searching for myeloma cells in the bone marrow slides through a microscope. Diagnosis of myeloma cells from bone marrow smears is a subjective and time-consuming task for pathologists. Also, because of depending on final decision on human eye and opinion, error risk in decision may occur. Sometimes, existence of infection in body causes plasma cell's increment which could be diagnosed wrongly as multiple myeloma. The computer diagnostic process will reduce the diagnostic time and also can be worked as a second opinion for pathologists. This study presents a computer-aided diagnostic method for myeloma cells diagnosis from bone marrow smears. At first, white blood cells consist of plasma cells and other marrow cells are separated from the red blood cells and background. Then, plasma cells are detected from other marrow cells by feature extraction and series of decision rules. Finally, normal plasma cells and myeloma cells could be classified easily by a classifier. This algorithm is applied on 50 digital images that are provided from bone marrow aspiration smears. These images contain 678 cells: 132 normal plasma cells, 256 myeloma cells and 290 other types of marrow cells. Applying the computer-aided diagnostic method for identifying myeloma cells on provided database showed a sensitivity of 96.52%; specificity of 93.04% and precision of 95.28%.
Insights
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.
