shiri gordon - Academia.edu (original) (raw)

Papers by shiri gordon

A bias field is an artifact inherent to MRI scanners which is manifested by a smooth intensity va... more A bias field is an artifact inherent to MRI scanners which is manifested by a smooth intensity variation across the scans. We present an innovative generative approach to address the inverse problem of bias field estimation and removal in a semi-supervised manner. The key contribution is the construction of a compound framework of four interacting, adversarial neural networks. Specifically, we simultaneously train a pair of neural networks, one for the reconstruction of the plain bias field and the other for the reconstruction of a bias-free MRI scan, such that the output of each together with the input biased scans define the loss of the other network. A third network, trained as a bias-field discriminator provides an additional loss to the bias field generator while an MRI segmentation network provides an additional loss to the bias-free MRI generator. We trained and validated our framework using real MRI scans with simulated bias fields and tested it on publicly available brain d...

IEEE Transactions on Medical Imaging, 2009

IEEE Transactions on Image Processing, 2006

In this paper we present a method for unsupervised image clustering. The method is based on a rec... more In this paper we present a method for unsupervised image clustering. The method is based on a recently introduced information-theoretic principle, the information bottleneck (IB) principle. Images are clustered such that the mutual information between the clusters and the image content is maximally preserved. The IB principle is applied to both discrete and continuous image representations, using dis-crete image histograms and probabilistic continuous image modeling based on mixture of Gaussian densities, respec-tively. Experimental results demonstrate the performance of the proposed method for image clustering on a large im-age database. Several clustering algorithms derived from the IB principle are explored and compared.

This work is motivated by the need for visual information management in the growing field of digi... more This work is motivated by the need for visual information management in the growing field of digital libraries and by the increasing information retrieval demands in the domains of medical imaging and telemedicine. We focus on a large database of digitized 35mm slides of the uterine cervix collected by the National Cancer Institute (NCI), National Institutes of Health (NIH), to study the evolution of lesions related to cervical cancer. As a first step towards this goal we focus on the problem of intelligently labeling (segmenting) regions of medical interest within the cervigram image. In this paper we use statistical tools for the segmentation of three tissue types of interest. 1.

The National Cancer Institute has collected a large database of uterine cervix images, termed “ce... more The National Cancer Institute has collected a large database of uterine cervix images, termed “cervigrams ” for cervical can-cer screening research. Tissues of interest within the cervi-gram, in particular the lesions, are of varying sizes and com-plex, non-convex shapes. The current work proposes a new methodology that enables the segmentation of non-convex re-gions, thus providing a major step forward towards cervigram tissue detection and lesion delineation. The framework transi-tions from pixels to a set of small coherent regions (superpix-els), which are grouped bottom-up into larger, non-convex, perceptually similar regions, utilizing a new graph-cut cri-terion and agglomerative clustering. Superpixels similarity is computed via a combined region and boundary informa-tion measure. Results for a set of 120 cervigrams, manually marked by a medical expert, are shown. Index Terms — cervicography images; segmentation; graph algorithms 1.

This thesis proposes a new method for unsupervised image clustering using probabilistic continuou... more This thesis proposes a new method for unsupervised image clustering using probabilistic continuous models and information theoretic principles. Image clustering relates to content-based image retrieval systems. It enables the implementation of efficient retrieval algorithms and the creation of a user friendly interface to the database. The thesis presents a coherent theory for continuous probabilistic image modeling based on mixture of Gaussians densities. The continuous image modeling is extended to the modeling of an image-set created by a supervised or an unsupervised clustering process. Three ways of obtaining the image-set model are introduced and the difference between them is discussed. Supervised image-set (category) modeling is utilized to compare between the proposed continuous models and the more traditional discrete image modeling based on histograms. The unsupervised image clustering framework is based on a continuous version of a recently introduced information theoret...

Machine Learning in Medical Imaging

Medical & biological engineering & computing, 2021

We present the Atlas of Classifiers (AoC)-a conceptually novel framework for brain MRI segmentati... more We present the Atlas of Classifiers (AoC)-a conceptually novel framework for brain MRI segmentation. The AoC is a spatial map of voxel-wise multinomial logistic regression (LR) functions learned from the labeled data. Upon convergence, the resulting fixed LR weights, a few for each voxel, represent the training dataset. It can, therefore, be considered as a light-weight learning machine, which despite its low capacity does not underfit the problem. The AoC construction is independent of the actual intensities of the test images, providing the flexibility to train it on the available labeled data and use it for the segmentation of images from different datasets and modalities. In this sense, it does not overfit the training data, as well. The proposed method has been applied to numerous publicly available datasets for the segmentation of brain MRI tissues and is shown to be robust to noise and outreach commonly used methods. Promising results were also obtained for multi-modal, cross...

Diseases in the Breast and Reproductive System V

Cervical cancer is the fourth most common cancer in women worldwide. In low-income countries, it ... more Cervical cancer is the fourth most common cancer in women worldwide. In low-income countries, it is a leading cause of death. Biop Medical has developed a portable multimodal optical imaging device for early screening and detection of squamous intraepithelial lesions (SIL). The device is a non-invasive probe which scans the cervix area and combines information from multiple optical modalities, for in vivo, real time detection of SIL. In this paper we present sample data acquired from 18 participants using the device in an in-vivo pilot study and present preliminary results of classification into low-grade and high-grade SIL.

Object recognition supported by user interaction for service robots

NeuroImage, 2018

MRI Segmentation of a pathological brain poses a significant challenge, as the available anatomic... more MRI Segmentation of a pathological brain poses a significant challenge, as the available anatomical priors that provide top-down information to aid segmentation are inadequate in the presence of abnormalities. This problem is further complicated for longitudinal data capturing impaired brain development or neurodegenerative conditions, since the dynamic of brain atrophies has to be considered as well. For these cases, the absence of compatible annotated training examples renders the commonly used multi-atlas or machine-learning approaches impractical. We present a novel segmentation approach that accounts for the lack of labeled data via multi-region multi-subject co-segmentation (MMCoSeg) of longitudinal MRI sequences. The underlying, unknown anatomy is learned throughout an iterative process, in which the segmentation of a region is supported both by the segmentation of the neighboring regions, which share common boundaries, and by the segmentation of corresponding regions, in the...

2016 IEEE 13th International Symposium on Biomedical Imaging (ISBI), 2016

Challenging biomedical segmentation problems can be addressed by combining top-down information b... more Challenging biomedical segmentation problems can be addressed by combining top-down information based on the known anatomy along with bottom-up models of the image data. Anatomical priors can be provided by probabilistic atlases. Nevertheless, in many cases the available atlases are inadequate. We present a novel method for the co-segmentation of multiple images into multiple regions, where only a very few annotated examples exist. The underlying, unknown anatomy is learned throughout an interleaved process, in which the segmentation of a region is supported both by the segmentation of the neighboring regions which share common boundaries and by the segmentation of corresponding regions in the other jointly segmented images. The method is applied to a mouse brain MRI dataset for the segmentation of five anatomical structures. Experimental results demonstrate the segmentation accuracy with respect to the data complexity.

2007 4th IEEE International Symposium on Biomedical Imaging: From Nano to Macro, 2007

ABSTRACT The National Cancer Institute has collected a large database of uterine cervix images, t... more ABSTRACT The National Cancer Institute has collected a large database of uterine cervix images, termed “cervigrams” for cervical can- cer screening research. Tissues of interest within the cervi- gram, in particular the lesions, are of varying sizes and com- plex, non-convex shapes. The current work proposes a new methodology,that enables the segmentation of non-convex re- gions, thus providing a major

... 5. SF Chang, JR Smith, and A. Beigi, Visual information retrieval from large distributed on-... more ... 5. SF Chang, JR Smith, and A. Beigi, Visual information retrieval from large distributed on-line reposi-tories, Communications of ACM ... P. King, S. Mitra, and B. Nutter, An automated, segmentation-based, rigid registration system for cervi-gram images utilizing simple clustering ...

In this work we focus on the generation of reliable ground truth data for a large medical reposit... more In this work we focus on the generation of reliable ground truth data for a large medical repository of digital cervicographic images (cervigrams) collected by the National Cancer Institute (NCI). This work is part of an ongoing effort conducted by NCI together with the National Library of Medicine (NLM) at the National Institutes of Health (NIH) to develop a web-based database of the digitized cervix images in order to study the evolution of lesions related to cervical cancer. As part of this effort, NCI has gathered twenty experts to manually segment a set of 933 cervigrams into regions of medical and anatomical interest. This process yields a set of images with multi-expert segmentations. The objectives of the current work are: 1) generate multi-expert ground truth and assess the diffculty of segmenting an image, 2) analyze observer variability in the multi-expert data, and 3) utilize the multi-expert ground truth to evaluate automatic segmentation algorithms. The work is based on STAPLE (Simultaneous Truth and Performance Level Estimation), which is a well known method to generate ground truth segmentation maps from multiple experts' observations. We have analyzed both intra- and inter-expert variability within the segmentation data. We propose novel measures of "segmentation complexity" by which we can automatically identify cervigrams that were found difficult to segment by the experts, based on their inter-observer variability. Finally, the results are used to assess our own automated algorithm for cervix boundary detection.

Image and Vision Computing, 2010

Computerized Medical Imaging and Graphics, 2009

This work is focused on the generation and utilization of a reliable ground truth (GT) segmentati... more This work is focused on the generation and utilization of a reliable ground truth (GT) segmentation for a large medical repository of digital cervicographic images (cervigrams) collected by the National Cancer Institute (NCI). NCI invited twenty experts to manually segment a set of 939 cervigrams into regions of medical and anatomical interest. Based on this unique data, the objectives of the current work are to: (1) Automatically generate a multi-expert GT segmentation map; (2) Use the GT map to automatically assess the complexity of a given segmentation task; (3) Use the GT map to evaluate the performance of an automated segmentation algorithm. The multi-expert GT map is generated via the STAPLE (Simultaneous Truth and Performance Level Estimation) algorithm, which is a well-known method to generate a GT segmentation from multiple observations. A new measure of segmentation complexity, which relies on the inter-observer variability within the GT map, is defined. This measure is used to identify images that were found difficult to segment by the experts and to compare the complexity of different segmentation tasks. An accuracy measure, which evaluates the performance of automated segmentation algorithms is presented. Two algorithms for cervix boundary detection are compared using the proposed accuracy measure. The measure is shown to reflect the actual segmentation quality achieved by the algorithms. The methods and conclusions presented in this work are general and can be applied to different images and segmentation tasks. Here they are applied to the cervigram database including a thorough analysis of the available data.