Neoplastic—Nuclei Cell Labelling Using Mask R-CNN

Abstract

AbstractNeoplastic cells are tumorous cells that damage the cells around them and are the prologue for cancer development in organs. However, identifying these cells poses a bottle-neck in the research of cancer cure as it is an extremely tedious job to manually isolate these from the rest of the cells in the tissue. Hence, the automation of this process using deep learning (DL)-based object detection and segmentation techniques such as Mask R-CNN will allow researchers and pathologists to save valuable time otherwise consumed in manually identifying these nuclei. The main objective of this research paper is to provide an instance segmentation technique to label and segment neoplastic cell nuclei from multiple instances of whole-slide images (WSI). For this process, a contemporary neural network architecture called the mask region-based convolutional neural network (Mask R-CNN) was used. This proposed technique generates a pixel-wise binary mask. These masks are capable of segmenting these instances and facilitating the advancement of intelligent systems in medical imaging and computational pathology. This time can instead be devoted to developing better cures by conducting more research. The paper also highlights the best techniques and practices that can be employed while training a model for a task of such complexity. The results of these techniques provide a mean average precision (mAP) score of 0.756 and a binary panoptic quality (bPQ) score of 0.675.KeywordsMedical imagingImage processingNeoplastic cellDeep learningComputer visionSegmentationComputational pathologyMask R-CNNCancer research