A Deep Graph Cut Model For 3D Brain Tumor Segmentation.

Brain tumor segmentation plays a key role in tumor diagnosis and surgical planning. In this paper, we propose a solution to the 3D brain tumor segmentation problem using deep learning and graph cut from the MRI data. In particular, the probability maps of a voxel to belong to the object (tumor) and background classes from the UNet are used to improve the energy function of the graph cut.

ADGAN: Attribute-Driven Generative Adversarial Network for Synthesis and Multiclass Classification of Pulmonary Nodules.

Lung cancer is the leading cause of cancer-related deaths worldwide. According to the American Cancer Society, early diagnosis of pulmonary nodules in computed tomography (CT) scans can improve the five-year survival rate up to 70% with proper treatment planning. In this article, we propose an attribute-driven Generative Adversarial Network (ADGAN) for synthesis and multiclass classification of Pulmonary Nodules.

An adaptive registration algorithm for zebrafish larval brain images.

Background And Objective: Zebrafish (Danio rerio) in their larval stages have grown increasingly popular as excellent vertebrate models for neurobiological research. Researchers can apply various tools in order to decode the neural structure patterns which can aid the understanding of vertebrate brain development. In order to do so, it is essential to map the gene expression patterns to an anatomical reference precisely.

Together Recognizing, Localizing and Summarizing Actions in Egocentric Videos.

Analysis of egocentric video has recently drawn attention of researchers in the computer vision as well as multimedia communities. In this paper, we propose a weakly supervised superpixel level joint framework for localization, recognition and summarization of actions in an egocentric video. We first recognize and localize single as well as multiple action(s) in each frame of an egocentric video and then construct a summary of these detected actions.

MDL-IWS: Multi-view Deep Learning with Iterative Watershed for Pulmonary Fissure Segmentation.

Pulmonary fissure segmentation is important for localization of lung lesions which include nodules at respective lobar territories. This can be very useful for diagnosis as well as treatment planning. In this paper, we propose a novel coarse-to-fine fissure segmentation approach by proposing a Multi-View Deep Learning driven Iterative WaterShed Algorithm (MDL-IWS).

CHAMPS: Cardiac health Hypergraph Analysis using Multimodal Physiological Signals.

State-of-the-art methods have reported various features for the non-invasive screening of Coronary Artery Disease (CAD). In this paper, we propose a novel approach to represent such features extracted from multiple physiological signals using hypergraph. Firstly, the biological and statistical interconnections among Photoplethysmogram (PPG) and Phonocardiogram (PCG) features are exploited by connecting them as hyperedges.

An Iterative Spanning Forest Framework for Superpixel Segmentation.

Superpixel segmentation has emerged as an important research problem in the areas of image processing and computer vision. In this paper, we propose a framework, namely Iterative Spanning Forest (ISF), in which improved sets of connected superpixels (supervoxels in 3D) can be generated by a sequence of image foresting transforms. In this framework, one can choose the most suitable combination of ISF components for a given application-i.

CHANGE: Cardiac Health Analysis Using Graph Eigenvalues.

Coronary Artery Disease (CAD) is an important problem in cardiac health and is a leading cause of human mortality. Prior arts have shown that features extracted from non-invasive Photoplethysmogram (PPG) signal are effective in classifying CAD. In this paper, we represent cardiac health as a graph (CHG) in order to exploit the dependencies of PPG features as well as the metadata features.

Nyström Approximated Temporally Constrained Multisimilarity Spectral Clustering Approach for Movie Scene Detection.

Movie scene detection has emerged as an important problem in present day multimedia applications. Since a movie typically consists of huge amount of video data with widespread content variations, detecting a movie scene has become extremely challenging. In this paper, we propose a fast yet accurate solution for movie scene detection using Nyström approximated multisimilarity spectral clustering with a temporal integrity constraint.

Cell tracking in microscopic video using matching and linking of bipartite graphs.

Automated visual tracking of cells from video microscopy has many important biomedical applications. In this paper, we track human monocyte cells in a fluorescent microscopic video using matching and linking of bipartite graphs. Tracking of cells over a pair of frames is modeled as a maximum cardinality minimum weight matching problem for a bipartite graph with a novel cost function.

Multi-organ segmentation from multi-phase abdominal CT via 4D graphs using enhancement, shape and location optimization.

The interpretation of medical images benefits from anatomical and physiological priors to optimize computer-aided diagnosis (CAD) applications. Diagnosis also relies on the comprehensive analysis of multiple organs and quantitative measures of soft tissue. An automated method optimized for medical image data is presented for the simultaneous segmentation of four abdominal organs from 4D CT data using graph cuts.

Reversible projection technique for colon unfolding.

Colon unfolding provides an efficient way to navigate the colon in computed tomographic colonography (CTC). Most existing unfolding techniques only compute forward projections. When radiologists find abnormalities or conduct measurements on the unfolded view (which is often quicker and easier), it is difficult to locate the corresponding region on the 3-D view for further examination (which is more accurate and reliable).

Virtual multi-fracture craniofacial reconstruction using computer vision and graph matching.

The problem of computer vision-guided reconstruction of a fractured human mandible from a computed tomography (CT) image sequence exhibiting multiple broken fragments is addressed. The problem resembles 3D jigsaw puzzle assembly and hence is of general interest for a variety of applications dealing with automated reconstruction or assembly. The specific problem of automated multi-fracture craniofacial reconstruction is particularly challenging since the identification of opposable fracture surfaces followed by their pairwise registration needs to be performed expeditiously in order to minimize the operative trauma to the patient and also limit the operating costs.

Computer vision guided virtual craniofacial reconstruction.

The problem of virtual craniofacial reconstruction from a sequence of computed tomography (CT) images is addressed and is modeled as a rigid surface registration problem. Two different classes of surface matching algorithms, namely the data aligned rigidity constrained exhaustive search (DARCES) algorithm and the iterative closest point (ICP) algorithm are first used in isolation. Since the human bone can be reasonably approximated as a rigid body, 3D rigid surface registration techniques such as the DARCES and ICP algorithms are deemed to be well suited for the purpose of aligning the fractured bone fragments.