Automatic liver tumor segmentation using global and patient specific Convolutional Neural Networks in follow-up CT studies

Abstract. We present a new, fully automatic algorithm for liver tumors segmentation in follow-up CT studies. The inputs are a baseline CT scan and a delineation of the tumors in it and a follow-up scan; the outputs are the tumors delineations in the…

3D segmentation using perceptual computing

Abstract We present a semi-automatic tool for 3D segmentation in volumetric medial sans using natural input from the user. The input onsists of hand motions and gestures aquired using 3D amera. Our method onsists of ve steps: 1) initial 2D…

Automatic liver tumor segmentation in follow-up CT studies using Convolutional Neural Networks

Abstract We present a new, fully automatic algorithm for liver tumors seg-mentation in follow-up CT studies. The inputs are a baseline CT scan and a de-lineation of the tumors in it and a follow-up scan; the outputs are the tumors…

Automatic lung tumor segmentation with leaks removal in follow-up CT studies

Abstract Purpose In modern oncology, disease progression and response to treatment are routinely evaluated with a series of volumetric scans. The number of tumors and their volume (mass) over time provides a quantitative measure for the evaluation. Thus, many of…

Automatic lung tumor segmentation with leaks removal in follow-up CT studies

Abstract We present a novel automatic algorithm for lung tumors segmentation in follow-up CT studies. The inputs are a baseline CT scan and a delineation of the tumors in it; the output is the tumor delineations in the follow-up scan.…

Carotid vasculature modeling from patient CT angiography studies for interventional procedures simulation

Abstract Objective A practical method for patient-specific modeling of the aortic arch and the entire carotid vasculature from computed tomography angiography (CTA) scans for morphologic analysis and for interventional procedure simulation. Materials and methods The method starts with the automatic…

Anatomical structures segmentation by spherical 3D ray casting and gradient domain editing

Abstract Fuzzy boundaries of anatomical structures in medical images make segmentation a challenging task. We present a new segmentation method that addresses the fuzzy boundaries problem. Our method maps the lengths of 3D rays cast from a seed point to…

Evaluation framework for carotid bifurcation lumen segmentation and stenosis grading

Abstract This paper describes an evaluation framework that allows a standardized and objective quantitative comparison of carotid artery lumen segmentation and stenosis grading algorithms. We describe the data repository comprising 56 multi-center, multi-vendor CTA datasets, their acquisition, the creation of…

Non-parametric iterative model constraint graph min-cut for automatic kidney segmentation

Abstract We present a new non-parametric model constraint graph min-cut algorithm for automatic kidney segmentation in CT images. The segmentation is formulated as a maximum a-posteriori estimation of a model-driven Markov random field. A non-parametric hybrid shape and intensity model…

Interventional endovascular procedures simulation with patient-specific carotid arteries models generated from CTA scans

An Iterative Model-Constrained Graph-Cut Algorithm for Adbominal Aortic Aneurysm Thrombus Segmentation

Abstract We present an iterative model-constrained graph-cut algorithm for the segmentation of Abdominal Aortic Aneurysm (AAA) thrombus. Given an initial segmentation of the aortic lumen, our method automatically segments the thrombus by iteratively coupling intensity-based graph min-cut segmentation and geometrical…

Nearly automatic vessels segmentation using graph-based energy minimization

Abstract We present a nearly automatic tool for the accurate segmentation of vascular structures in volumetric CTA images. Its inputs are a start and an end seed points inside the vessel. The two-step graph-based energy minimization method starts by computing…

Vessels-Cut: a graph based approach to patient-specific carotid arteries modeling

Abstract We present a nearly automatic graph-based segmentation method for patient specific modeling of the aortic arch and carotid arteries from CTA scans for interventional radiology simulation. The method starts with morphological-based segmentation of the aorta and the construction of…

A variational method for vessels segmentation: algorithm and application to liver vessels visualization

Abstract We present a new variational-based method for automatic liver vessels segmentation from abdominal CTA images. The segmentation task is formulated as a functional minimization problem within a variational framework. We introduce a new functional that incorporates both geometrical vesselness…

An iterative Bayesian approach for nearly automatic liver segmentation: algorithm and validation

Abstract Purpose We present a new algorithm for nearly automatic liver segmentation and volume estimation from abdominal Computed Tomography Angiography (CTA) images and its validation. Materials and methods Our hybrid algorithm uses a multiresolution iterative scheme. It starts from a…

An iterative Bayesian approach for livers segmentation: algorithm and clinical validation study

Abstract We present a new method and validation study for the nearly automatic segmentation of liver tumors. The method is part of a nearly automatic system for simultaneous segmentation of liver contours, vessels, and tumors from abdominal CTA scans. It…

Page 1 of 212