Features for cells and nuclei classification

Structure Embedded Nucleus Classification for Histopathology Images

Nuclei classification provides valuable information for histopathology image analysis. However, the large variations in the appearance of different nuclei types cause difficulties in identifying nuclei. Most neural network based methods are affected by the local receptive field of convolutions, and pay less attention to the spatial distribution of nuclei or the irregular contour shape of a nucleus. In this paper, we first propose a novel polygon-structure feature learning mechanism that transforms a nucleus contour into a sequence of points sampled in order, and employ a recurrent neural network that aggregates the sequential change in distance between key points to obtain learnable shape features. Next, we convert a histopathology image into a graph structure with nuclei as nodes, and build a graph neural network to embed the spatial distribution of nuclei into their representations. To capture the correlations between the categories of nuclei and their surrounding tissue patterns, we further introduce edge features that are defined as the background textures between adjacent nuclei. Lastly, we integrate both polygon and graph structure learning mechanisms into a whole framework that can extract intra and inter-nucleus structural characteristics for nuclei classification. Experimental results show that the proposed framework achieves significant improvements compared to the previous methods. Code and data are made available via https://github.com/lhaof/SENC.

Shape and Boundary Similarity Features for Accurate HCC Image Recognition

Nucleus morphology is of great importance in conventional cancer pathological diagnosis, which could provide information difference between normal and abnormal nuclei visually. Therefore, this paper proposes two novel kinds of features for normal and hepatocellular carcinoma (HCC) nucleus recognition, including shape and boundary similarity. First, each individual nucleus patch with the fixed size is obtained using center-proliferation segmentation (CPS) method. Then, nucleus shape library is constructed based on manual selection by pathologists, which is utilized to measure nucleus shape similarity via Dice, Jaccard, precision, and recall coefficients. Meanwhile, boundary similarity is evaluated through triangles composed of some boundary feature points for each nucleus. Finally, the conventional random forest (RF) is used to train and test the classification model for HCC nucleus recognition. Extensive cross-validation tests could facilitate the selection of the optimal feature set and the experiment comparison results demonstrate that our proposed morphological features are more beneficial for classification compared with other traditional characteristics.

A Machine Learning Assisted, Label-free, Non-invasive Approach for Somatic Reprogramming in Induced Pluripotent Stem Cell Colony Formation Detection and Prediction

During cellular reprogramming, the mesenchymal-to-epithelial transition is accompanied by changes in morphology, which occur prior to iPSC colony formation. The current approach for detecting morphological changes associated with reprogramming purely relies on human experiences, which involve intensive amounts of upfront training, human error with limited quality control and batch-to-batch variations. Here, we report a time-lapse-based bright-field imaging analysis system that allows us to implement a label-free, non-invasive approach to measure morphological dynamics. To automatically analyse and determine iPSC colony formation, a machine learning-based classification, segmentation, and statistical modelling system was developed to guide colony selection. The system can detect and monitor the earliest cellular texture changes after the induction of reprogramming in human somatic cells on day 7 from the 20–24 day process. Moreover, after determining the reprogramming process and iPSC colony formation quantitatively, a mathematical model was developed to statistically predict the best iPSC selection phase independent of any other resources. All the computational detection and prediction experiments were evaluated using a validation dataset, and biological verification was performed. These algorithm-detected colonies show no significant differences (Pearson Coefficient) in terms of their biological features compared to the manually processed colonies using standard molecular approaches.