Automated Plasma Cell Segmentation for Multiple Myeloma Diagnosis: A Deep Learning Approach Using a Novel Dataset

The development of computer-assisted diagnostic tools for cancer detection has gained significant momentum, with image processing playing a pivotal role in automating the analysis of microscopic images. This work focuses on Multiple Myeloma (MM), a type of blood cancer affecting plasma cells, and addresses the critical challenge of plasma cell segmentation in microscopic images. Accurate segmentation is essential for quantifying malignant versus healthy cells, a key step in MM diagnosis and treatment planning. Plasma cell segmentation is inherently challenging due to the variability in the size, shape, and staining of plasma cells, the presence of clustered cells with overlapping boundaries, and the interference caused by unstained background elements such as red blood cells. Traditional manual segmentation techniques are time-consuming, subjective, and prone to inter-observer variability, underscoring the urgent need for automated, reliable solutions. In response to these challenges, I introduce a novel dataset comprising 775 microscopic images of bone marrow aspirate slides, collected from MM patients. These images were captured using two different cameras (Olympus and Nikon) to ensure robustness against device-specific variations and underwent stain color normalization to address inconsistencies in staining. To leverage this dataset, I propose an automated segmentation pipeline based on YOLOv8, a state-of-the-art deep learning model renowned for its speed and accuracy in object detection tasks. The methodology involves preprocessing the images, extracting bounding boxes from annotated masks, converting annotations into YOLO format, and training the model to detect and segment both the nucleus and cytoplasm of plasma cells. Model performance is evaluated using precision, recall, and mean Average Precision (mAP) metrics, supplemented by qualitative assessments through visual comparisons of predicted and ground truth annotations. Our study contributes significantly to the advancement of AI-driven cancer diagnostics, providing a robust, efficient, and scalable solution for plasma cell segmentation in MM. By enhancing the accuracy and efficiency of MM diagnosis, this work has the potential to improve early detection, support clinical decision-making, and ultimately lead to better patient outcomes.