Papers by Gali Zimmerman-Moreno
Cancers
Female BRCA1/BRCA2 (=BRCA) pathogenic variants (PVs) carriers are at a substantially higher risk ... more Female BRCA1/BRCA2 (=BRCA) pathogenic variants (PVs) carriers are at a substantially higher risk for developing breast cancer (BC) compared with the average risk population. Detection of BC at an early stage significantly improves prognosis. To facilitate early BC detection, a surveillance scheme is offered to BRCA PV carriers from age 25–30 years that includes annual MRI based breast imaging. Indeed, adherence to the recommended scheme has been shown to be associated with earlier disease stages at BC diagnosis, more in-situ pathology, smaller tumors, and less axillary involvement. While MRI is the most sensitive modality for BC detection in BRCA PV carriers, there are a significant number of overlooked or misinterpreted radiological lesions (mostly enhancing foci), leading to a delayed BC diagnosis at a more advanced stage. In this study we developed an artificial intelligence (AI)-network, aimed at a more accurate classification of enhancing foci, in MRIs of BRCA PV carriers, thus...
Proceedings. 17th IEEE Symposium on Computer-Based Medical Systems
The National Cancer Institute has collected a large database of digitized 35mm slides of the uter... more The National Cancer Institute has collected a large database of digitized 35mm slides of the uterine cervix, the idea being to build a system enabling to study the evolution of lesions related to cervical cancer. In taking the first few steps towards this goal, the objective of this work is to develop and evaluate methodologies required for visual-based (i.e. contentbased) indexing and retrieval that substantially improve information management of such a database. In this paper we model the properties of three tissue types using color and texture features, and use these models for image segmentation. Statistical modeling and segmentation tools are used for the task.
1 11.1 Overview In this lecture we will describe methods for performing a binary classification t... more 1 11.1 Overview In this lecture we will describe methods for performing a binary classification task on a linearly non-separable data by the means of linear classification. We first explore the linear problem and the mathematical methods used to solve this problem. We then perform a generalization that will allow us to deal with more complex, noisy or non-linear situations, by embedding the input data into a higher dimensional feature-space, in which the data is separable (the concept is demonstrated in Figure 11.1). This will be accomplished a “kernel trick”
Sparse Representation for White Matter Fiber Compression and Calculation of Inter-Fiber Similarity
Recent years have brought about impressive reconstructions of white matter architecture, due to t... more Recent years have brought about impressive reconstructions of white matter architecture, due to the advance of increasingly sophisticated MRI based acquisition methods and modeling techniques. These result in extremely large sets of streamelines (fibers ) for each subject. The sets require large amount of storage and are often unwieldy and difficult to manipulate and analyze. We propose to use sparse representations for fibers to achieve a more compact representation. We also propose the means for calculating inter-fiber similarities in the compressed space using a measure, which we term: Cosine with Dictionary Similarity Weighting (CWDS). The performance of both sparse representations and CWDS is evaluated on full brain fiber-sets of 15 healthy subjects. The results show that a reconstruction error of slightly below 2 mm is achieved, and that CWDS is highly correlated with the cosine similarity in the original space.
Chest Radiograph Pathology Categorization via Transfer Learning
Deep Learning for Medical Image Analysis, 2017
Abstract The goal of this chapter is to give an overview of the research we have been conducting ... more Abstract The goal of this chapter is to give an overview of the research we have been conducting in automated X-ray pathology detection for the past 10 years, from bag-of-visual-words (BoVW) models to the Convolutional Neural Network (CNN) Deep Learning schemes. Our study was one of the first to suggest the possibility of using non-medical training, using transfer learning from the general imagery to the medical domain. In this chapter we explore deep features that are extracted from intermediate CNN layers in comparison to a set of classical shallow features, including GLCM, PHOG, GABOR, GIST and the more recent, state-of-the-art BoVW model. We investigate the possible benefits of using feature selection techniques on the Deep CNN feature layers. Average AUC results of close to 90% are shown for categorization of 6 different pathologies in a dataset of more than 600 radiographs. This study shows the strength and robustness of the CNN features. We conclude that deep learning with large scale non-medical image databases may be a good substitute, or addition, to domain specific representations which are yet to be available for general medical image recognition tasks. With the BoW schemes, our method won first place in ImageClef competitions. With the DL architectures, we are now able to use the system in real clinical settings.
This work is motivated by the need for visual information extraction and management in the growin... more This work is motivated by the need for visual information extraction and management in the growing field of medical image archives. In particular the work focuses on a unique medical repository of digital cervicographic images ("Cervigrams") collected by the National Cancer Institute (NCI) in a longitudinal multi-year study carried out in Guanacaste, Costa Rica. NCI together with the National Library of Medicine (NLM) is developing a unique Web-based database of the digitized cervix images to study the evolution of lesions related to cervical cancer. Such a database requires specific tools that can analyze the cervigram content and represent it in a way that can be efficiently searched and compared. We present a multi-step scheme for segmenting and labeling regions of medical and anatomical interest within the cervigram, utilizing statistical tools and adequate features. The multi-step structure is motivated by the large diversity of the images within the database. The algorithm identifies the cervix region within the image. It than separates the cervix region into three main tissue types: the columnar epithelium (CE), the squamous epithelium (SE), and the acetowhite (AW), which is visible for a short time following the application of acetic acid. The algorithm is developed and tested on a subset of 120 cervigrams that were manually labeled by NCI experts. Initial segmentation results are presented and evaluated.
3rd IEEE International Symposium on Biomedical Imaging: Macro to Nano, 2006.
This work is motivated by the need for visual information extraction and management in the growin... more This work is motivated by the need for visual information extraction and management in the growing field of content based image retrieval from medical archives. In particular it focuses on a unique medical repository of cervicographic images ("Cervigrams") collected by the National Cancer Institute, National Institutes of Health, to study the evolution of lesions related to cervical cancer. The paper briefly presents a framework for cervigram segmentation and labelling, focusing on the identification of two anatomical landmarks: the cervix boundary and the os. These landmarks are identified based on their convexity, using adequate mathematical tools. Segmentation results are exemplified and an initial validation is carried out on a subset of 120 manually labelled cervigrams.
Classification of contrast-enhanced spectral mammography (CESM) images
International Journal of Computer Assisted Radiology and Surgery
Automatic classification of cancer cells in multispectral microscopic images of lymph node samples
Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual Conference, Aug 1, 2016
Histopathological analysis is crucial for the diagnosis of a large number of cancer types. A lot ... more Histopathological analysis is crucial for the diagnosis of a large number of cancer types. A lot of progress has been made in the development of molecular based assays, but many of the cases still require the careful analysis of the stained tissue under a bright-field microscope and its analysis. This procedure is costly and time-consuming. We present a novel method for classification of cancer cells in lymph node images. It is based on the measurement of the spectral image of hematoxylin and eosin stained sample under the microscope and the analysis of the acquired data using state of the art machine learning techniques. The method is based on the analysis of the spectral information of the cells as well as their morphological properties. A large number of descriptors is extracted for each cell location, which are used to train a supervised classifier which discriminates between normal and cancer cells. We show that a reliable analysis can be made with detection rate (recall) of 81...
White Matter Fiber Set Simplification by Redundancy Reduction with Minimum Anatomical Information Loss
Mathematics and Visualization, 2016
The Fiber-Density-Coreset for Redundancy Reduction in huge Fiber-Sets
NeuroImage, 2016
Whole brain fiber-based comparison (FBC)-A tool for diffusion tensor imaging-based cohort studies
Human brain mapping, Jan 31, 2015
We present a novel method for fiber-based comparison of diffusion tensor imaging (DTI) scans of g... more We present a novel method for fiber-based comparison of diffusion tensor imaging (DTI) scans of groups of subjects. The method entails initial preprocessing and fiber reconstruction by tractography of each brain in its native coordinate system. Several diffusion parameters are sampled along each fiber and used in subsequent comparisons. A spatial correspondence between subjects is established based on geometric similarity between fibers in a template set (several choices for template are explored), and fibers in all other subjects. Diffusion parameters between groups are compared statistically for each template fiber. Results are presented at single fiber resolution. As an initial exploratory step in neurological population studies this method points to the locations affected by the pathology of interest, without requiring a hypothesis. It does not make any grouping assumptions on the fibers and no manual intervention is needed. The framework was applied here to 18 healthy subjects ...
Pneumothorax detection in chest radiographs using local and global texture signatures
Medical Imaging 2015: Computer-Aided Diagnosis, 2015
2008 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, 2008
A method is proposed for modeling and classification of White Matter fiber tracts in the brain. T... more A method is proposed for modeling and classification of White Matter fiber tracts in the brain. The presented scheme uses Classification Trees in conjunction with spatial representation of the individual fibers, in order to capture the characteristic behavior of fibers belonging to a specific anatomical structure. The method is characterized by high classification speed, under 3 seconds for all the fibers in a typical DTI of a brain. The model has the ability to represent complex geometric structures and has an intuitive interpretation. Encouraging results are demonstrated for tract classification on real data from ten different subjects.
Fiber Based Comparison of Whole Brain Tractographies with Application to Amyotrophic Lateral Sclerosis
Mathematics and Visualization, 2013
IEEE Transactions on Medical Imaging, 2000
A supervised framework is presented for the automatic registration and segmentation of white matt... more A supervised framework is presented for the automatic registration and segmentation of white matter (WM) tractographies extracted from brain DT-MRI. The framework relies on the direct registration between the fibers, without requiring any intensity-based registration as preprocessing. An affine transform is recovered together with a set of segmented fibers. A recently introduced probabilistic boosting tree classifier is used in a segmentation refinement step to improve the precision of the target tract segmentation. The proposed method compares favorably with a state-of-the-art intensity-based algorithm for affine registration of DTI tractographies. Segmentation results for 12 major WM tracts are demonstrated. Quantitative results are also provided for the segmentation of a particularly difficult case, the optic radiation tract. An average precision of 80% and recall of 55% were obtained for the optimal configuration of the presented method.
Novel system for measuring giant spectral images and its application for cancer detection
Imaging, Manipulation, and Analysis of Biomolecules, Cells, and Tissues XV, 2017
Coresets versus clustering: comparison of methods for redundancy reduction in very large white matter fiber sets
Medical Imaging 2016: Image Processing, 2016
<title>Automatic detection of specular reflections in uterine cervix images</title>
Medical Imaging 2006: Image Processing, 2006
Specular reflections strongly affect the appearance of images, and usually hinder the computer vi... more Specular reflections strongly affect the appearance of images, and usually hinder the computer vision algorithms applied to them. This is particularly the case with uterine cervix images. The highlights created by specular reflections are a major obstacle in the way of automatic segmentation of such images. We propose a method for the detection of specularities in cervix images that utilizes intensity, saturation and gradient information. A two-stage segmentation process is proposed for the identification of highlights. First, coarse regions that contain the reflections are defined. Second, probabilistic modeling and segmentation is used to achieve a precise segmentation inside the coarse regions. The resulting regions are filled by propagating the surrounding color information. The efficiency of the method for cervix images is demonstrated.
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Papers by Gali Zimmerman-Moreno