Cell Segmentation Using a Similarity Interface With a Multi-Task Convolutional Neural Network.

Even though convolutional neural networks (CNN) have been used for cell segmentation, they require pixel-level ground truth annotations. This paper proposes a multitask learning algorithm for cell detection and segmentation using CNNs. We use dot annotations placed inside each cell indicating approximate cell centroids to create training datasets for the detection and segmentation tasks. The segmentation task is used to map the input image to foreground versus background regions, whereas the detection task is used to predict the centroids of the cells. Our multitask model shares convolutional layers between the two tasks, while having task-specific output layers. Learning two tasks simultaneously reduces the risks of overfitting and also helps in separating overlapping cells better. We also introduce a similarity interface (SI) that can be integrated with our multitask network to allow easy adaptation between domains, and to compensate for the variability in contrast and texture of cells seen in microscopy images. The SI comprises an unsupervised first layer in combination with a neighborhood similarity layer (NSL). A layer of logistic sigmoid functions is used as an unsupervised first layer to separate clustered image patches from each other. The NSL transforms its input feature map at a given pixel by computing its similarity to the surrounding neighborhood. Our proposed method achieves higher/comparable detection and segmentation scores as compared to recent state-of-the-art methods with significantly reduced effort for generating training data.

Cell Detection Using Extremal Regions in a Semisupervised Learning Framework.

This paper discusses an algorithm to build a semisupervised learning framework for detecting cells. The cell candidates are represented as extremal regions drawn from a hierarchical image representation. Training a classifier for cell detection using supervised approaches relies on a large amount of training data, which requires a lot of effort and time. We propose a semisupervised approach to reduce this burden. The set of extremal regions is generated using a maximally stable extremal region (MSER) detector. A subset of nonoverlapping regions with high similarity to the cells of interest is selected. Using the tree built from the MSER detector, we develop a novel differentiable unsupervised loss term that enforces the nonoverlapping constraint with the learned function. Our algorithm requires very few examples of cells with simple dot annotations for training. The supervised and unsupervised losses are embedded in a Bayesian framework for probabilistic learning.

DISJUNCTIVE NORMAL SHAPE MODELS.

A novel implicit parametric shape model is proposed for segmentation and analysis of medical images. Functions representing the shape of an object can be approximated as a union of N polytopes. Each polytope is obtained by the intersection of M half-spaces. The shape function can be approximated as a disjunction of conjunctions, using the disjunctive normal form. The shape model is initialized using seed points defined by the user. We define a cost function based on the Chan-Vese energy functional. The model is differentiable, hence, gradient based optimization algorithms are used to find the model parameters.

CELL TRACKING USING PARTICLE FILTERS WITH IMPLICIT CONVEX SHAPE MODEL IN 4D CONFOCAL MICROSCOPY IMAGES.

Bayesian frameworks are commonly used in tracking algorithms. An important example is the particle filter, where a stochastic motion model describes the evolution of the state, and the observation model relates the noisy measurements to the state. Particle filters have been used to track the lineage of cells. Propagating the shape model of the cell through the particle filter is beneficial for tracking. We approximate arbitrary shapes of cells with a novel implicit convex function. The importance sampling step of the particle filter is defined using the cost associated with fitting our implicit convex shape model to the observations. Our technique is capable of tracking the lineage of cells for nonmitotic stages. We validate our algorithm by tracking the lineage of retinal and lens cells in zebrafish embryos.

Isolation and two-step classification of normal white blood cells in peripheral blood smears.

INTRODUCTION: An automated system for differential white blood cell (WBC) counting based on morphology can make manual differential leukocyte counts faster and less tedious for pathologists and laboratory professionals. We present an automated system for isolation and classification of WBCs in manually prepared, Wright stained, peripheral blood smears from whole slide images (WSI). METHODS: A simple, classification scheme using color information and morphology is proposed. The performance of the algorithm was evaluated by comparing our proposed method with a hematopathologist's visual classification. The isolation algorithm was applied to 1938 subimages of WBCs, 1804 of them were accurately isolated. Then, as the first step of a two-step classification process, WBCs were broadly classified into cells with segmented nuclei and cells with nonsegmented nuclei. The nucleus shape is one of the key factors in deciding how to classify WBCs. Ambiguities associated with connected nuclear lobes are resolved by detecting maximum curvature points and partitioning them using geometric rules. The second step is to define a set of features using the information from the cytoplasm and nuclear regions to classify WBCs using linear discriminant analysis. This two-step classification approach stratifies normal WBC types accurately from a whole slide image. RESULTS: System evaluation is performed using a 10-fold cross-validation technique. Confusion matrix of the classifier is presented to evaluate the accuracy for each type of WBC detection. Experiments show that the two-step classification implemented achieves a 93.9% overall accuracy in the five subtype classification. CONCLUSION: Our methodology achieves a semiautomated system for the detection and classification of normal WBCs from scanned WSI. Further studies will be focused on detecting and segmenting abnormal WBCs, comparison of 20× and 40× data, and expanding the applications for bone marrow aspirates.