Instance Segmentation of Multiple Myeloma Cells Using Deep-Wise Data Augmentation and Mask R-CNN

Abstract

Multiple myeloma is a condition of cancer in the bone marrow that can lead to dysfunction of the body and fatal expression in the patient. Manual microscopic analysis of abnormal plasma cells, also known as multiple myeloma cells, is one of the most commonly used diagnostic methods for multiple myeloma. However, as it is a manual process, it consumes too much effort and time. Besides, it has a higher chance of human errors. This paper presents a computer-aided detection and segmentation of myeloma cells from microscopic images of the bone marrow aspiration. Two major contributions are presented in this paper. First, different Mask R-CNN models using different images, including original microscopic images, contrast-enhanced images and stained cell images, are developed to perform instance segmentation of multiple myeloma cells. As a second contribution, a deep-wise augmentation, a deep learning-based data augmentation method, is applied to increase the performance of Mask R-CNN models. Based on the experimental findings, the Mask R-CNN model using contrast-enhanced images combined with the proposed deep-wise data augmentation provides a superior performance compared to other models. It achieves a mean precision of 0.9973, mean recall of 0.8631, and mean intersection over union (IOU) of 0.9062.

Enhancing diagnostic accuracy of multiple myeloma through ML-driven analysis of hematological slides: new dataset and identification model to support hematologists

“…Although several studies bring new perspectives in the transition from this manual technique to an automated format using AI, several image repositories were produced with high-cost equipment, often inaccessible to laboratories that serve lower-income populations, and images from slides with higher quality than usually observed in the daily routine of hematology professionals. Furthermore, most bone marrow slide datasets identify only plasma cells, not allowing the automatic evaluation of the percentage of plasma cells 28 , 29 .…”

Diagnosis of Middle Ear Diseases Based on Convolutional Neural Network

“…Convolutional neural networks (CNNs) are popular DL algorithms in the medical sphere. The Mask R-CNN algorithm is commonly used to detect and segment target objects, including particular cell types [11] and brain metastases [12]. However, its use in the field of otorhinolaryngology has been minimal [13].…”