Search results for "Segmentation"
showing 10 items of 674 documents
A smart and operator independent system to delineate tumours in Positron Emission Tomography scans
2018
Abstract Positron Emission Tomography (PET) imaging has an enormous potential to improve radiation therapy treatment planning offering complementary functional information with respect to other anatomical imaging approaches. The aim of this study is to develop an operator independent, reliable, and clinically feasible system for biological tumour volume delineation from PET images. Under this design hypothesis, we combine several known approaches in an original way to deploy a system with a high level of automation. The proposed system automatically identifies the optimal region of interest around the tumour and performs a slice-by-slice marching local active contour segmentation. It automa…
MULTI-SCALE ANALYSIS OF LUNG COMPUTED TOMOGRAPHY IMAGES
2007
A computer-aided detection (CAD) system for the identification of lung internal nodules in low-dose multi-detector helical Computed Tomography (CT) images was developed in the framework of the MAGIC-5 project. The three modules of our lung CAD system, a segmentation algorithm for lung internal region identification, a multi-scale dot-enhancement filter for nodule candidate selection and a multi-scale neural technique for false positive finding reduction, are described. The results obtained on a dataset of low-dose and thin-slice CT scans are shown in terms of free response receiver operating characteristic (FROC) curves and discussed.
Comparison of different segmentation approaches without using gold standard. Application to the estimation of the left ventricle ejection fraction fr…
2011
International audience; A statistical method is proposed to compare several estimates of a relevant clinical parameter when no gold standard is available. The method is illustrated by considering the left ventricle ejection fraction derived from cardiac magnetic resonance images and computed using seven approaches with different degrees of automation. The proposed method did not use any a priori regarding with the reliability of each method and its degree of automation. The results showed that the most accurate estimates of the ejection fraction were obtained using manual segmentations, followed by the semiautomatic methods, while the methods with the least user input yielded the least accu…
Reference standard space hippocampus labels according to the European Alzheimer's Disease Consortium–Alzheimer's Disease Neuroimaging Initiative harm…
2017
Abstract Introduction A harmonized protocol (HarP) for manual hippocampal segmentation on magnetic resonance imaging (MRI) has recently been developed by an international European Alzheimer's Disease Consortium–Alzheimer's Disease Neuroimaging Initiative project. We aimed at providing consensual certified HarP hippocampal labels in Montreal Neurological Institute (MNI) standard space to serve as reference in automated image analyses. Methods Manual HarP tracings on the high-resolution MNI152 standard space template of four expert certified HarP tracers were combined to obtain consensual bilateral hippocampus labels. Utility and validity of these reference labels is demonstrated in a simple …
Diagnostic strategy in segmentation defect of the vertebrae: a retrospective study of 73 patients
2018
BackgroundSegmentation defects of the vertebrae (SDV) are non-specific features found in various syndromes. The molecular bases of SDV are not fully elucidated due to the wide range of phenotypes and classification issues. The genes involved are in the Notch signalling pathway, which is a key system in somitogenesis. Here we report on mutations identified in a diagnosis cohort of SDV. We focused on spondylocostal dysostosis (SCD) and the phenotype of these patients in order to establish a diagnostic strategy when confronted with SDV.Patients and methodsWe used DNA samples from a cohort of 73 patients and performed targeted sequencing of the five known SCD-causing genes (DLL3,MESP2,LFNG,HES7…
Workflow-centred open-source fully automated lung volumetry in chest CT
2019
Aim To develop a robust open-source method for fully automated extraction of total lung capacity (TLC) from computed tomography (CT) images and to demonstrate its integration into the clinical workflow. Materials and methods Using only open-source software, an algorithm was developed based on a region-growing method that does not require manual interaction. Lung volumes calculated from reconstructions with different kernels (TLCCT) were assessed. To validate the algorithm calculations, the results were correlated to TLC measured by pulmonary function testing (TLCPFT) in a subgroup of patients for which this information was available within 3 days of the CT examination. Results A total of 28…
A supervised learning framework of statistical shape and probability priors for automatic prostate segmentation in ultrasound images
2013
Prostate segmentation aids in prostate volume estimation, multi-modal image registration, and to create patient specific anatomical models for surgical planning and image guided biopsies. However, manual segmentation is time consuming and suffers from inter-and intra-observer variabilities. Low contrast images of trans rectal ultrasound and presence of imaging artifacts like speckle, micro-calcifications, and shadow regions hinder computer aided automatic or semi-automatic prostate segmentation. In this paper, we propose a prostate segmentation approach based on building multiple mean parametric models derived from principal component analysis of shape and posterior probabilities in a multi…
Deep Learning Techniques for Automatic MRI Cardiac Multi-Structures Segmentation and Diagnosis: Is the Problem Solved?
2018
Delineation of the left ventricular cavity, myocardium, and right ventricle from cardiac magnetic resonance images (multi-slice 2-D cine MRI) is a common clinical task to establish diagnosis. The automation of the corresponding tasks has thus been the subject of intense research over the past decades. In this paper, we introduce the “Automatic Cardiac Diagnosis Challenge” dataset (ACDC), the largest publicly available and fully annotated dataset for the purpose of cardiac MRI (CMR) assessment. The dataset contains data from 150 multi-equipments CMRI recordings with reference measurements and classification from two medical experts. The overarching objective of this paper is to measure how f…
Virtual temporal bone: an interactive 3-dimensional learning aid for cranial base surgery.
2009
OBJECTIVE: We have developed an interactive virtual model of the temporal bone for the training and teaching of cranial base surgery. METHODS: The virtual model was based on the tomographic data of the Visible Human Project. The male Visible Human's computed tomographic data were volumetrically reconstructed as virtual bone tissue, and the individual photographic slices provided the basis for segmentation of the middle and inner ear structures, cranial nerves, vessels, and brainstem. These structures were created by using outlining and tube editing tools, allowing structural modeling either directly on the basis of the photographic data or according to information from textbooks and cadaver…
Training labels for hippocampal segmentation based on the EADC-ADNI harmonized hippocampal protocol
2015
Abstract Background The European Alzheimer's Disease Consortium and Alzheimer's Disease Neuroimaging Initiative (ADNI) Harmonized Protocol (HarP) is a Delphi definition of manual hippocampal segmentation from magnetic resonance imaging (MRI) that can be used as the standard of truth to train new tracers, and to validate automated segmentation algorithms. Training requires large and representative data sets of segmented hippocampi. This work aims to produce a set of HarP labels for the proper training and certification of tracers and algorithms. Methods Sixty-eight 1.5 T and 67 3 T volumetric structural ADNI scans from different subjects, balanced by age, medial temporal atrophy, and scanner…