Effects of Noninhibitory Serpin Maspin on the Actin Cytoskeleton: A Quantitative Image Modeling Approach

Recent developments in quantitative image analysis allow us to interrogate confocal microscopy images to answer biological questions. Clumped and layered cell nuclei and cytoplasm in confocal images challenges the ability to identify subcellular compartments. To date, there is no perfect image analysis method to identify cytoskeletal changes in confocal images. Here, we present a multidisciplinary study where an image analysis model was developed to allow quantitative measurements of changes in the cytoskeleton of cells with different maspin exposure. Maspin, a noninhibitory serpin influences cell migration, adhesion, invasion, proliferation, and apoptosis in ways that are consistent with its identification as a tumor metastasis suppressor. Using different cell types, we tested the hypothesis that reduction in cell migration by maspin would be reflected in the architecture of the actin cytoskeleton. A hybrid marker-controlled watershed segmentation technique was used to segment the nuclei, cytoplasm, and ruffling regions before measuring cytoskeletal changes. This was informed by immunohistochemical staining of cells transfected stably or transiently with maspin proteins, or with added bioactive peptides or protein. Image analysis results showed that the effects of maspin were mirrored by effects on cell architecture, in a way that could be described quantitatively

Visualization and Correction of Automated Segmentation, Tracking and Lineaging from 5-D Stem Cell Image Sequences

Results: We present an application that enables the quantitative analysis of multichannel 5-D (x, y, z, t, channel) and large montage confocal fluorescence microscopy images. The image sequences show stem cells together with blood vessels, enabling quantification of the dynamic behaviors of stem cells in relation to their vascular niche, with applications in developmental and cancer biology. Our application automatically segments, tracks, and lineages the image sequence data and then allows the user to view and edit the results of automated algorithms in a stereoscopic 3-D window while simultaneously viewing the stem cell lineage tree in a 2-D window. Using the GPU to store and render the image sequence data enables a hybrid computational approach. An inference-based approach utilizing user-provided edits to automatically correct related mistakes executes interactively on the system CPU while the GPU handles 3-D visualization tasks. Conclusions: By exploiting commodity computer gaming hardware, we have developed an application that can be run in the laboratory to facilitate rapid iteration through biological experiments. There is a pressing need for visualization and analysis tools for 5-D live cell image data. We combine accurate unsupervised processes with an intuitive visualization of the results. Our validation interface allows for each data set to be corrected to 100% accuracy, ensuring that downstream data analysis is accurate and verifiable. Our tool is the first to combine all of these aspects, leveraging the synergies obtained by utilizing validation information from stereo visualization to improve the low level image processing tasks.Comment: BioVis 2014 conferenc

Adaptive Hybrid Blood Cell Image Segmentation

Image segmentation is an important phase in the image recognition system. In medical imaging such as blood cell analysis, it becomes a crucial step in quantitative cytophotometry. Currently, blood cell images become predominantly valuable in medical diagnostics tools. In this paper, we present an adaptive hybrid analysis based on selected segmentation algorithms. Three designates common approaches, that are Fuzzy c-means, K-means and Mean-shift are adapted. Blood cell images that are infected with malaria parasites at various stages were tested. The most suitable method will be selected based on the lowest number of regions. The selected approach will be enhanced by applying Median-cut algorithm to further expand the segmentation process. The proposed adaptive hybrid method has shown a significant improvement in the number of region

Leveraging Supervoxels for Medical Image Volume Segmentation With Limited Supervision

The majority of existing methods for machine learning-based medical image segmentation are supervised models that require large amounts of fully annotated images. These types of datasets are typically not available in the medical domain and are difficult and expensive to generate. A wide-spread use of machine learning based models for medical image segmentation therefore requires the development of data-efficient algorithms that only require limited supervision. To address these challenges, this thesis presents new machine learning methodology for unsupervised lung tumor segmentation and few-shot learning based organ segmentation. When working in the limited supervision paradigm, exploiting the available information in the data is key. The methodology developed in this thesis leverages automatically generated supervoxels in various ways to exploit the structural information in the images. The work on unsupervised tumor segmentation explores the opportunity of performing clustering on a population-level in order to provide the algorithm with as much information as possible. To facilitate this population-level across-patient clustering, supervoxel representations are exploited to reduce the number of samples, and thereby the computational cost. In the work on few-shot learning-based organ segmentation, supervoxels are used to generate pseudo-labels for self-supervised training. Further, to obtain a model that is robust to the typically large and inhomogeneous background class, a novel anomaly detection-inspired classifier is proposed to ease the modelling of the background. To encourage the resulting segmentation maps to respect edges defined in the input space, a supervoxel-informed feature refinement module is proposed to refine the embedded feature vectors during inference. Finally, to improve trustworthiness, an architecture-agnostic mechanism to estimate model uncertainty in few-shot segmentation is developed. Results demonstrate that supervoxels are versatile tools for leveraging structural information in medical data when training segmentation models with limited supervision

Medical Image Segmentation: Thresholding and Minimum Spanning Trees

I bildesegmentering deles et bilde i separate objekter eller regioner. Det er et essensielt skritt i bildebehandling for å definere interesseområder for videre behandling eller analyse. Oppdelingsprosessen reduserer kompleksiteten til et bilde for å forenkle analysen av attributtene oppnådd etter segmentering. Det forandrer representasjonen av informasjonen i det opprinnelige bildet og presenterer pikslene på en måte som er mer meningsfull og lettere å forstå. Bildesegmentering har forskjellige anvendelser. For medisinske bilder tar segmenteringsprosessen sikte på å trekke ut bildedatasettet for å identifisere områder av anatomien som er relevante for en bestemt studie eller diagnose av pasienten. For eksempel kan man lokalisere berørte eller anormale deler av kroppen. Segmentering av oppfølgingsdata og baseline lesjonssegmentering er også svært viktig for å vurdere behandlingsresponsen. Det er forskjellige metoder som blir brukt for bildesegmentering. De kan klassifiseres basert på hvordan de er formulert og hvordan segmenteringsprosessen utføres. Metodene inkluderer de som er baserte på terskelverdier, graf-baserte, kant-baserte, klynge-baserte, modell-baserte og hybride metoder, og metoder basert på maskinlæring og dyp læring. Andre metoder er baserte på å utvide, splitte og legge sammen regioner, å finne diskontinuiteter i randen, vannskille segmentering, aktive kontuter og graf-baserte metoder. I denne avhandlingen har vi utviklet metoder for å segmentere forskjellige typer medisinske bilder. Vi testet metodene på datasett for hvite blodceller (WBCs) og magnetiske resonansbilder (MRI). De utviklede metodene og analysen som er utført på bildedatasettet er presentert i tre artikler. I artikkel A (Paper A) foreslo vi en metode for segmentering av nukleuser og cytoplasma fra hvite blodceller. Metodene estimerer terskelen for segmentering av nukleuser automatisk basert på lokale minima. Metoden segmenterer WBC-ene før segmentering av cytoplasma avhengig av kompleksiteten til objektene i bildet. For bilder der WBC-ene er godt skilt fra røde blodlegemer (RBC), er WBC-ene segmentert ved å ta gjennomsnittet av $n$ bilder som allerede var filtrert med en terskelverdi. For bilder der RBC-er overlapper WBC-ene, er hele WBC-ene segmentert ved hjelp av enkle lineære iterative klynger (SLIC) og vannskillemetoder. Cytoplasmaet oppnås ved å trekke den segmenterte nukleusen fra den segmenterte WBC-en. Metoden testes på to forskjellige offentlig tilgjengelige datasett, og resultatene sammenlignes med toppmoderne metoder. I artikkel B (Paper B) foreslo vi en metode for segmentering av hjernesvulster basert på minste dekkende tre-konsepter (minimum spanning tree, MST). Metoden utfører interaktiv segmentering basert på MST. I denne artikkelen er bildet lastet inn i et interaktivt vindu for segmentering av svulsten. Fokusregion og bakgrunn skilles ved å klikke for å dele MST i to trær. Ett av disse trærne representerer fokusregionen og det andre representerer bakgrunnen. Den foreslåtte metoden ble testet ved å segmentere to forskjellige 2D-hjerne T1 vektede magnetisk resonans bildedatasett. Metoden er enkel å implementere og resultatene indikerer at den er nøyaktig og effektiv. I artikkel C (Paper C) foreslår vi en metode som behandler et 3D MRI-volum og deler det i hjernen, ikke-hjernevev og bakgrunnsegmenter. Det er en grafbasert metode som bruker MST til å skille 3D MRI inn i de tre regiontypene. Grafen lages av et forhåndsbehandlet 3D MRI-volum etterfulgt av konstrueringen av MST-en. Segmenteringsprosessen gir tre merkede, sammenkoblende komponenter som omformes tilbake til 3D MRI-form. Etikettene brukes til å segmentere hjernen, ikke-hjernevev og bakgrunn. Metoden ble testet på tre forskjellige offentlig tilgjengelige datasett og resultatene ble sammenlignet med ulike toppmoderne metoder.In image segmentation, an image is divided into separate objects or regions. It is an essential step in image processing to define areas of interest for further processing or analysis. The segmentation process reduces the complexity of an image to simplify the analysis of the attributes obtained after segmentation. It changes the representation of the information in the original image and presents the pixels in a way that is more meaningful and easier to understand. Image segmentation has various applications. For medical images, the segmentation process aims to extract the image data set to identify areas of the anatomy relevant to a particular study or diagnosis of the patient. For example, one can locate affected or abnormal parts of the body. Segmentation of follow-up data and baseline lesion segmentation is also very important to assess the treatment response. There are different methods used for image segmentation. They can be classified based on how they are formulated and how the segmentation process is performed. The methods include those based on threshold values, edge-based, cluster-based, model-based and hybrid methods, and methods based on machine learning and deep learning. Other methods are based on growing, splitting and merging regions, finding discontinuities in the edge, watershed segmentation, active contours and graph-based methods. In this thesis, we have developed methods for segmenting different types of medical images. We tested the methods on datasets for white blood cells (WBCs) and magnetic resonance images (MRI). The developed methods and the analysis performed on the image data set are presented in three articles. In Paper A we proposed a method for segmenting nuclei and cytoplasm from white blood cells. The method estimates the threshold for segmentation of nuclei automatically based on local minima. The method segments the WBCs before segmenting the cytoplasm depending on the complexity of the objects in the image. For images where the WBCs are well separated from red blood cells (RBCs), the WBCs are segmented by taking the average of $n$ images that were already filtered with a threshold value. For images where RBCs overlap the WBCs, the entire WBCs are segmented using simple linear iterative clustering (SLIC) and watershed methods. The cytoplasm is obtained by subtracting the segmented nucleus from the segmented WBC. The method is tested on two different publicly available datasets, and the results are compared with state of the art methods. In Paper B, we proposed a method for segmenting brain tumors based on minimum spanning tree (MST) concepts. The method performs interactive segmentation based on the MST. In this paper, the image is loaded in an interactive window for segmenting the tumor. The region of interest and the background are selected by clicking to split the MST into two trees. One of these trees represents the region of interest and the other represents the background. The proposed method was tested by segmenting two different 2D brain T1-weighted magnetic resonance image data sets. The method is simple to implement and the results indicate that it is accurate and efficient. In Paper C, we propose a method that processes a 3D MRI volume and partitions it into brain, non-brain tissues, and background segments. It is a graph-based method that uses MST to separate the 3D MRI into the brain, non-brain, and background regions. The graph is made from a preprocessed 3D MRI volume followed by constructing the MST. The segmentation process produces three labeled connected components which are reshaped back to the shape of the 3D MRI. The labels are used to segment the brain, non-brain tissues, and the background. The method was tested on three different publicly available data sets and the results were compared to different state of the art methods.Doktorgradsavhandlin

Combining crowd worker, algorithm, and expert efforts to find boundaries of objects in images

While traditional approaches to image analysis have typically relied upon either manual annotation by experts or purely-algorithmic approaches, the rise of crowdsourcing now provides a new source of human labor to create training data or perform computations at run-time. Given this richer design space, how should we utilize algorithms, crowds, and experts to better annotate images? To answer this question for the important task of finding the boundaries of objects or regions in images, I focus on image segmentation, an important precursor to solving a variety of fundamental image analysis problems, including recognition, classification, tracking, registration, retrieval, and 3D visualization. The first part of the work includes a detailed analysis of the relative strengths and weaknesses of three different approaches to demarcate object boundaries in images: by experts, by crowdsourced laymen, and by automated computer vision algorithms. The second part of the work describes three hybrid system designs that integrate computer vision algorithms and crowdsourced laymen to demarcate boundaries in images. Experiments revealed that hybrid system designs yielded more accurate results than relying on algorithms or crowd workers alone and could yield segmentations that are indistinguishable from those created by biomedical experts. To encourage community-wide effort to continue working on developing methods and systems for image-based studies which can have real and measurable impact that benefit society at large, datasets and code are publicly-shared (http://www.cs.bu.edu/~betke/BiomedicalImageSegmentation/)

Artificial Intelligence Algorithms for Eye Banking

Eye banking plays a critical role in modern medicine by providing cornea tissues for transplantation to restore vision for millions of people worldwide. The evaluation of corneal endothelium is done by measuring the corneal endothelial cell density (ECD). Unfortunately, the current system to measure ECD is manual, time-consuming, and error prone. Furthermore, the impact of social behaviors and biological conditions on corneal endothelium and corneal transplant success is largely unexplored. To overcome these challenges, this dissertation aims to develop tools for corneal endothelial image and data analysis that enhance the efficiency and quality of the cornea transplants. In the first study, an image processing algorithm is developed to analyze corneal endothelial images captured by a Konan CellChek specular microscope. The algorithm successfully identifies the region of interest, filters the image, and employs stochastic watershed segmentation to determine cell boundaries and evaluate endothelial cell density (ECD). The proposed algorithm achieves a high correlation with manual counts (R2 = 0.98) and has an average analysis time of 2.5 seconds. In the second study, a deep learning-based cell segmentation algorithm called Mobile-CellNet is proposed to estimate ECD. This technique addresses the limitations of classical algorithms and creates a more robust and highly efficient algorithm. The approach achieves a mean absolute error of 4.06% for ECD on the test set, similar to U-Net but with significantly fewer floating-point operations and parameters. The third study explores the correlation between alcohol abuse and corneal endothelial morphology in a donor pool of 5,624 individuals. Multivariable regression analysis shows that alcohol abuse is associated with a reduction in endothelial cell density, an increase in the coefficient of variation, and a decrease in percent hexagonality. These studies highlight the potential of big data and artificial algorithms in accurately and efficiently analyzing corneal images and donor medical data to improve the efficiency of eye banking and patient outcomes. By automating the analysis of corneal images and exploring the impact of social behaviors and biological conditions on corneal endothelial morphology, we can enhance the quality and availability of cornea transplants and ultimately improve the lives of millions of people worldwide

A Systematic Review on Automatic Detection of Plasmodium Parasite

Plasmodium parasite is the main cause of malaria which has taken many lives. Some research works have been conducted to detect the Plasmodium parasite automatically. This research aims to identify the development of current research in the area of Plasmodium parasite detection. The research uses a systematic literature review (SLR) approach comprising three stages, namely planning, conducting, and reporting. The search process is based on the keywords which were determined in advance. The selection process involves the inclusion and exclusion criteria. The search yields 45 literatures from five different digital libraries. The identification process finds out that 28 methods are applied and mainly categorizes as machine learning algorithms with performance achievements between 60% and 95%. Overall, the research of Plasmodium parasite detection today has focused on the development with artificial intelligence specifically related to machine and deep learning. These approaches are believed as the most effective approach to detect Plasmodium parasites

Liver segmentation using marker controlled watershed transform

The largest organ in the body is the liver and primarily helps in metabolism and detoxification. Liver segmentation is a crucial step in liver cancer detection in computer vision-based biomedical image analysis. Liver segmentation is a critical task and results in under-segmentation and over-segmentation due to the complex structure of abdominal computed tomography (CT) images, noise, and textural variations over the image. This paper presents liver segmentation in abdominal CT images using marker-based watershed transforms. In the pre-processing stage, a modified double stage gaussian filter (MDSGF) is used to enhance the contrast, and preserve the edge and texture information of liver CT images. Further, marker controlled watershed transform is utilized for the segmentation of liver images from the abdominal CT images. Liver segmentation using suggested MDSGF and marker-based watershed transform help to diminish the under-segmentation and over-segmentation of the liver object. The performance of the proposed system is evaluated on the LiTS dataset based on Dice score (DS), relative volume difference (RVD), volumetric overlapping error (VOE), and Jaccard index (JI). The proposed method gives (Dice score of 0.959, RVD of 0.09, VOE of 0.089, and JI of 0.921)