We present an efficient deep learning framework for identifying, segmenting, and classifying cell membranes and nuclei from human epidermal growth factor receptor-2 (HER2)-stained breast cancer images with minimal user intervention. This is a long-standing issue for pathologists because the manual quantification of HER2 is error-prone, costly, and time-consuming. Hence, we propose a deep learning-based HER2 deep neural network (Her2Net) to solve this issue. The convolutional and deconvolutional parts of the proposed Her2Net framework consisted mainly of multiple convolution layers, max-pooling layers, spatial pyramid pooling layers, deconvolution layers, up-sampling layers, and trapezoidal long short-term memory (TLSTM). A fully connected layer and a softmax layer were also used for classification and error estimation. Finally, HER2 scores were calculated based on the classification results. The main contribution of our proposed Her2Net framework includes the implementation of TLSTM and a deep learning framework for cell membrane and nucleus detection, segmentation, and classification and HER2 scoring. Our proposed Her2Net achieved 96.64% precision, 96.79% recall, 96.71% F-score, 93.08% negative predictive value, 98.33% accuracy, and a 6.84% false-positive rate. Our results demonstrate the high accuracy and wide applicability of the proposed Her2Net in the context of HER2 scoring for breast cancer evaluation.

Being a non-histone protein, Ki-67 is one of the essential biomarkers for the immunohistochemical assessment of proliferation rate in breast cancer screening and grading. The Ki-67 signature is always sensitive to radiotherapy and chemotherapy. Due to random morphological, color and intensity variations of cell nuclei (immunopositive and immunonegative), manual/subjective assessment of Ki-67 scoring is error-prone and time-consuming. Hence, several machine learning approaches have been reported; nevertheless, none of them had worked on deep learning based hotspots detection and proliferation scoring. In this article, we suggest an advanced deep learning model for computerized recognition of candidate hotspots and subsequent proliferation rate scoring by quantifying Ki-67 appearance in breast cancer immunohistochemical images. Unlike existing Ki-67 scoring techniques, our methodology uses Gamma mixture model (GMM) with Expectation-Maximization for seed point detection and patch selection and deep learning, comprises with decision layer, for hotspots detection and proliferation scoring. Experimental results provide 93% precision, 0.88% recall and 0.91% F-score value. The model performance has also been compared with the pathologists’ manual annotations and recently published articles. In future, the proposed deep learning framework will be highly reliable and beneficial to the junior and senior pathologists for fast and efficient Ki-67 scoring.

Erythrocytes (red blood cells, RBCs), the most common type of blood cells in humans are well known for their ability in transporting oxygen to the whole body through hemoglobin. Alterations in their membrane skeletal proteins modify shape and mechanical properties resulting in several diseases. Atomic force microscopy (AFM), a new emerging technique allows non-invasive imaging of cell, its membrane and characterization of surface roughness at micrometer/nanometer resolution with minimal sample preparation. AFM imaging provides direct measurement of single cell morphology, its alteration and quantitative data on surface properties. Hence, AFM studies of human RBCs have picked up pace in the last decade. The aim of this paper is to review the various applications of AFM for characterization of human RBCs topology. AFM has been used for studying surface characteristics like nanostructure of membranes, cytoskeleton, microstructure, fluidity, vascular endothelium, etc., of human RBCs. Various modes of AFM imaging has been used to measure surface properties like stiffness, roughness, and elasticity. Topological alterations of erythrocytes in response to different pathological conditions have also been investigated by AFM. Thus, AFM-based studies and application of image processing techniques can effectively provide detailed insights about the morphology and membrane properties of human erythrocytes at nanoscale.