Nuclei/Cell Detection in Microscopic Skeletal Muscle Fiber Images and Histopathological Brain Tumor Images Using Sparse Optimizations

Nuclei/Cell detection is usually a prerequisite procedure in many computer-aided biomedical image analysis tasks. In this thesis we propose two automatic nuclei/cell detection frameworks. One is for nuclei detection in skeletal muscle fiber images and the other is for brain tumor histopathological images. For skeletal muscle fiber images, the major challenges include: i) shape and size variations of the nuclei, ii) overlapping nuclear clumps, and iii) a series of z-stack images with out-of-focus regions. We propose a novel automatic detection algorithm consisting of the following components: 1) The original z-stack images are first converted into one all-in-focus image. 2) A sufficient number of hypothetical ellipses are then generated for each nuclei contour. 3) Next, a set of representative training samples and discriminative features are selected by a two-stage sparse model. 4) A classifier is trained using the refined training data. 5) Final nuclei detection is obtained by mean-shift clustering based on inner distance. The proposed method was tested on a set of images containing over 1500 nuclei. The results outperform the current state-of-the-art approaches. For brain tumor histopathological images, the major challenges are to handle significant variations in cell appearance and to split touching cells. The proposed novel automatic cell detection consists of: 1) Sparse reconstruction for splitting touching cells. 2) Adaptive dictionary learning for handling cell appearance variations. The proposed method was extensively tested on a data set with over 2000 cells. The result outperforms other state-of-the-art algorithms with F1 score = 0.96

Improving cancer subtype diagnosis and grading using clinical decision support system based on computer-aided tissue image analysis

This research focuses towards the development of a clinical decision support system (CDSS) based on cellular and tissue image analysis and classification system that improves consistency and facilitates the clinical decision making process. In a typical cancer examination, pathologists make diagnosis by manually reading morphological features in patient biopsy images, in which cancer biomarkers are highlighted by using different staining techniques. This process is subjected to pathologist's training and experience, especially when the same cancer has several subtypes (i.e. benign tumor subtype vs. malignant subtype) and the same cancer tissue biopsy contains heterogeneous morphologies in different locations. The variability in pathologist's manual reading may result in varying cancer diagnosis and treatment. This Ph.D. research aims to reduce the subjectivity and variation existing in traditional histo-pathological reading of patient tissue biopsy slides through Computer-Aided Diagnosis (CAD). Using the CAD, quantitative molecular profiling of cancer biomarkers of stained biopsy images are obtained by extracting and analyzing texture and cellular structure features. In addition, cancer sub-type classification and a semi-automatic grade scoring (i.e. clinical decision making) for improved consistency over a large number of cancer subtype images can be performed. The CAD tools do have their own limitations and in certain cases the clinicians, however, prefer systems which are flexible and take into account their individuality when necessary by providing some control rather than fully automated system. Therefore, to be able to introduce CDSS in health care, we need to understand users' perspectives and preferences on the new information technology. This forms as the basis for this research where we target to present the quantitative information acquired through the image analysis, annotate the images and provide suitable visualization which can facilitate the process of decision making in a clinical setting.PhDCommittee Chair: Dr. May D. Wang; Committee Member: Dr. Andrew N. Young; Committee Member: Dr. Anthony J. Yezzi; Committee Member: Dr. Edward J. Coyle; Committee Member: Dr. Paul Benkese

Automated Clump Splitting for Biological Cell Segmentation in Microscopy Using Image Analysis

Formation of clumps due to touching or overlapping of individual objects in an image is common. The process is natural in some cell cultures, for instance, yeast cells typically grow in clumps. Automated analysis of images containing such clumps requires the capability to split them into their constituent objects. Failure of the segmentation methods to split the clumps leads to the requirement of developing clump splitting methods to be used as post-processing step towards overall segmentation. The goal of this thesis work is to study and develop an automated method for splitting cell clumps in images of biological cells. To achieve this goal we studied previous clump splitting methods found in the literature. One of the best methods is based on defining split lines by detecting and linking concavity points. We found that this method has deficiencies in it and first modified it to achieve improved clump splitting results. We also developed a novel method for clump splitting following a similar approach. Like any other concavity point-based clump splitting method, both these methods start with finding all the concavity points on the contour of the clumps. Contrary to the original method, these methods look for every possible valid concavity point in a concavity region using curvature analysis, thus minimizing false split lines as well as under-segmentation. The modified method then uses Delaunay triangulation to narrow down the list of all the possible split lines between all the concavity points to a list of candidate split lines. Finally, it uses a set of features such as saliency and alignment to define a cost function. The best split line is found for each concavity point yielding the minimum value for the cost function. On the other hand, the novel method uses variable size rectangular window to search for the concavity point-pairs forming the split lines. This makes the method less dependent on user-defined parameters. We also propose some post-processing steps that remove some non-cellular objects based on a priori information on cell shapes. We compared the performance of these two methods with the performance of the original method and of a widely used method that is based on the watershed transform. Three different sets of images of yeast cells were used. Precision and recall analysis was used to show that the two methods proposed in this thesis outperform the two methods taken from the literature. Although the targeted application of the methods is splitting of cell clumps, it can be applied to split clumps of other convex objects as well. /Kir1

Nuclei & Glands Instance Segmentation in Histology Images: A Narrative Review

Instance segmentation of nuclei and glands in the histology images is an important step in computational pathology workflow for cancer diagnosis, treatment planning and survival analysis. With the advent of modern hardware, the recent availability of large-scale quality public datasets and the community organized grand challenges have seen a surge in automated methods focusing on domain specific challenges, which is pivotal for technology advancements and clinical translation. In this survey, 126 papers illustrating the AI based methods for nuclei and glands instance segmentation published in the last five years (2017-2022) are deeply analyzed, the limitations of current approaches and the open challenges are discussed. Moreover, the potential future research direction is presented and the contribution of state-of-the-art methods is summarized. Further, a generalized summary of publicly available datasets and a detailed insights on the grand challenges illustrating the top performing methods specific to each challenge is also provided. Besides, we intended to give the reader current state of existing research and pointers to the future directions in developing methods that can be used in clinical practice enabling improved diagnosis, grading, prognosis, and treatment planning of cancer. To the best of our knowledge, no previous work has reviewed the instance segmentation in histology images focusing towards this direction.Comment: 60 pages, 14 figure

Contributions to Medical Image Segmentation and Signal Analysis Utilizing Model Selection Methods

This thesis presents contributions to model selection techniques, especially based on information theoretic criteria, with the goal of solving problems appearing in signal analysis and in medical image representation, segmentation, and compression.The field of medical image segmentation is wide and is quickly developing to make use of higher available computational power. This thesis concentrates on several applications that allow the utilization of parametric models for image and signal representation. One important application is cell nuclei segmentation from histological images. We model nuclei contours by ellipses and thus the complicated problem of separating overlapping nuclei can be rephrased as a model selection problem, where the number of nuclei, their shapes, and their locations define one segmentation. In this thesis, we present methods for model selection in this parametric setting, where the intuitive algorithms are combined with more principled ones, namely those based on the minimum description length (MDL) principle. The results of the introduced unsupervised segmentation algorithm are compared with human subject segmentations, and are also evaluated with the help of a pathology expert.Another considered medical image application is lossless compression. The objective has been to add the task of image segmentation to that of image compression such that the image regions can be transmitted separately, depending on the region of interest for diagnosis. The experiments performed on retinal color images show that our modeling, in which the MDL criterion selects the structure of the linear predictive models, outperforms publicly available image compressors such as the lossless version of JPEG 2000.For time series modeling, the thesis presents an algorithm which allows detection of changes in time series signals. The algorithm is based on one of the most recent implementations of the MDL principle, the sequentially normalized maximum likelihood (SNML) models.This thesis produces contributions in the form of new methods and algorithms, where the simplicity of information theoretic principles are combined with a rather complex and problem dependent modeling formulation, resulting in both heuristically motivated and principled algorithmic solutions