IEEE Applied Imagery Pattern Recognition Workshop : [proceedings]

Various neurological diseases affect the morphology of myelinated axons. Quantitative analysis of these structures and changes occurring due to neurodegeneration or neuroregeneration is of great importance for characterization of disease state and treatment response. This paper proposes a robust, meta-learning based pipeline for segmentation of axons and surrounding myelin sheaths in electron microscopy images. This is the first step towards computation of electron microscopy related bio-markers of hypoglossal nerve degeneration/regeneration. This segmentation task is challenging due to large variations in morphology and texture of myelinated axons at different levels of degeneration and very limited availability of annotated data. To overcome these difficulties, the proposed pipeline uses a meta learning-based training strategy and a U-net like encoder decoder deep neural network. Experiments on unseen test data collected at different magnification levels (i.e, trained on 500X and 1200X images, and tested on 250X and 2500X images) showed improved segmentation performance by 5% to 7% compared to a regularly trained, comparable deep learning network.

Detection, segmentation, and quantification of microvascular structures are the main steps towards studying microvascular remodeling. Combined with appropriate staining, confocal microscopy imaging enables exploration of the full 3D anatomical characteristics of microvascular systems. Segmentation of confocal microscopy images is a challenging task due to complexity of anatomical structures, staining and imaging issues, and lack of annotated training data. In this paper, we propose a deep learning system for robust segmentation of cranial vasculature of mice in confocal microscopy images. The proposed system is an ensemble of two deep-learning cascades consisting of two coarse-to-fine subnetworks with skip connections in between. One cascade aims to improve sensitivity, while the other aims to improve precision of the segmentation results. Our experiments on mice cranial vasculature showed promising results achieving segmentation accuracy of 92.02% and dice score of 81.45% despite being trained on very limited confocal microscopy data.

Analysis of morphometric features of nuclei plays an important role in understanding disease progression and predict efficacy of treatment. First step towards this goal requires segmentation of individual nuclei within the imaged tissue. Accu-rate nuclei instance segmentation is one of the most challenging tasks in computational pathology due to broad morphological variances of individual nuclei and dense clustering of nuclei with indistinct boundaries. It is extremely laborious and costly to annotate nuclei instances, requiring experienced pathologists to manually draw the contours, which often results in the lack of annotated data. Inevitably subjective annotation and mislabeling prevent supervised learning approaches to learn from accurate samples and consequently decrease the generalization capacity to robustly segment unseen organ nuclei, leading to over- or under-segmentations as a result. To address these issues, we use a variation of U-Net that uses squeeze and excitation blocks (USE-Net) for robust nuclei segmentation. The squeeze and excitation blocks allow the network to perform feature recalibration by emphasizing informative features and suppressing less useful ones. Furthermore, we extend the proposed network USE-Net not to generate only a segmentation mask, but also to output shape markers to allow better separation of nuclei from each other particularly within dense clusters. The proposed network was trained, tested, and evaluated on 2018 MICCAI Multi-Organ-Nuclei-Segmentation (MoNuSeg) challenge dataset. Promising results were obtained on unseen data despite that the data used for training USE-Net was significantly small. The source code of the USE-Net is available at https://github.com/CIva-Lab/USE-Net.

Malaria is a major health threat caused by Plasmod-ium parasites that infect the red blood cells. Two predominant types of Plasmodium parasites are Plasmodium vivax (P. vivax) and Plasmodium falciparum (P. falciparum). Diagnosis of malaria typically involves visual microscopy examination of blood smears for malaria parasites. This is a tedious, error-prone visual inspection task requiring microscopy expertise which is often lacking in resource-poor settings. To address these problems, attempts have been made in recent years to automate malaria diagnosis using machine learning approaches. Several challenges need to be met for a machine learning approach to be successful in malaria diagnosis. Microscopy images acquired at different sites often vary in color, contrast, and consistency caused by different smear preparation and staining methods. Moreover, touching and overlapping cells complicate the red blood cell detection process, which can lead to inaccurate blood cell counts and thus incorrect parasitemia calculations. In this work, we propose a red blood cell detection and extraction framework to enable processing and analysis of single cells for follow-up processes like counting infected cells or identifying parasite species in thin blood smears. This framework consists of two modules: a cell detection module and a cell extraction module. The cell detection module trains a modified Channel-wise Feature Pyramid Network for Medicine (CFPNet-M) deep learning network that takes the green channel of the image and the color-deconvolution processed image as inputs, and learns a truncated distance transform image of cell annotations. CFPNet-M is chosen due to its low resource requirements, while the distance transform allows achieving more accurate cell counts for dense cells. Once the cells are detected by the network, the cell extraction module is used to extract single cells from the original image and count the number of cells. Our preliminary results based on 193 patients (including 148 P. Falciparum infected patients, and 45 uninfected patients) show that our framework achieves cell count accuracy of 92.2%.

Segmentation and quantification of microvasculature structures are the main steps toward studying microvasculature remodeling. The proposed patch based semantic architecture enables accurate segmentation for the challenging epifluorescence microscopy images. Our pixel-based fast semantic network trained on random patches from different epifluorescence images to learn how to discriminate between vessels versus nonvessels pixels. The proposed semantic vessel network (SVNet) relies on understanding the morphological structure of the thin vessels in the patches rather than considering the whole image as input to speed up the training process and to maintain the clarity of thin structures. Experimental results on our ovariectomized - ovary removed (OVX) - mice dura mater epifluorescence microscopy images shows promising results in both arteriole and venule part. We compared our results with different segmentation methods such as local, global thresholding, matched based filter approaches and related state of the art deep learning networks. Our overall accuracy (> 98%) outperforms all the methods including our previous work (VNet). [1].

In January 2016 the U.S. National Library of Medicine announced a challenge competition calling for the development and discovery of high-quality algorithms and software that rank how well consumer images of prescription pills match reference images of pills in its authoritative RxIMAGE collection. This challenge was motivated by the need to easily identify unknown prescription pills both by healthcare personnel and the general public. Potential benefits of this capability include confirmation of the pill in settings where the documentation and medication have been separated, such as in a disaster or emergency; and confirmation of a pill when the prescribed medication changes from brand to generic, or for any other reason the shape and color of the pill change. The data for the competition consisted of two types of images, high quality macro photographs, reference images, and consumer quality photographs of the quality we expect users of a proposed application to acquire. A training dataset consisting of 2000 reference images and 5000 corresponding consumer quality images acquired from 1000 pills was provided to challenge participants. A second dataset acquired from 1000 pills with similar distributions of shape and color was reserved as a segregated testing set. Challenge submissions were required to produce a ranking of the reference images, given a consumer quality image as input. Determination of the winning teams was done using the mean average precision quality metric, with the three winners obtaining mean average precision scores of 0.27, 0.09, and 0.08. In the retrieval results, the correct image was amongst the top five ranked images 43%, 12%, and 11% of the time, out of 5000 query/consumer images. This is an initial promising step towards development of an NLM software system and application-programming interface facilitating pill identification. The training dataset will continue to be freely available online at: http://pir.nlm.nih.gov/challenge/submission.html.

In this paper, we consider confocal microscopy based vessel segmentation with optimized features and random forest classification. By utilizing multi-scale vessel-specific features tuned to capture curvilinear structures such as Frobenius norm of the Hessian eigenvalues, Laplacian of Gaussians (LoG), oriented second derivative, line detector and intensity masked with LoG scale map. we obtain better segmentation results in challenging imaging conditions. We obtain binary segmentations using random forest classifier trained on physiologists marked ground-truth. Experimental results on mice dura mater confocal microscopy vessel segmentations indicate that we obtain better results compared to global segmentation approaches.