Multicentric clinical evaluation of a computed tomography-based fully automated deep neural network for aortic maximum diameter and volumetric measurements.

Objective: This study aims to evaluate a fully automatic deep learning-based method (augmented radiology for vascular aneurysm [ARVA]) for aortic segmentation and simultaneous diameter and volume measurements.

Methods: A clinical validation dataset was constructed from preoperative and postoperative aortic computed tomography angiography (CTA) scans for assessing these functions. The dataset totaled 350 computed tomography angiography scans from 216 patients treated at two different hospitals.

Pre-surgical and Post-surgical Aortic Aneurysm Maximum Diameter Measurement: Full Automation by Artificial Intelligence.

Objective: The aim of this study was to evaluate an automatic, deep learning based method (Augmented Radiology for Vascular Aneurysm [ARVA]), to detect and assess maximum aortic diameter, providing cross sectional outer to outer aortic wall measurements.

Methods: Accurate external aortic wall diameter measurement is performed along the entire aorta, from the ascending aorta to the iliac bifurcations, on both pre- and post-operative contrast enhanced computed tomography angiography (CTA) scans. A training database of 489 CTAs was used to train a pipeline of neural networks for automatic external aortic wall measurements.

Imaging Biomarkers of Tumor Response in Neuroendocrine Liver Metastases Treated with Transarterial Chemoembolization: Can Enhancing Tumor Burden of the Whole Liver Help Predict Patient Survival?

Purpose To investigate whether whole-liver enhancing tumor burden [ETB] can serve as an imaging biomarker and help predict survival better than World Health Organization (WHO), Response Evaluation Criteria in Solid Tumors (RECIST), modified RECIST (mRECIST), and European Association for the Study of the Liver (EASL) methods in patients with multifocal, bilobar neuroendocrine liver metastases (NELM) after the first transarterial chemoembolization (TACE) procedure. Materials and Methods This HIPAA-compliant, institutional review board-approved retrospective study included 51 patients (mean age, 57.8 years ± 13.

Tagged template deformation.

Model-based approaches are very popular for medical image segmentation as they carry useful prior information on the target structure. Among them, the implicit template deformation framework recently bridged the gap between the efficiency and flexibility of level-set region competition and the robustness of atlas deformation approaches. This paper generalizes this method by introducing the notion of tagged templates.

Registration of free-breathing 3D+t abdominal perfusion CT images via co-segmentation.

Dynamic contrast-enhanced computed tomography (DCE-CT) is a valuable imaging modality to assess tissues properties, particularly in tumours, by estimating pharmacokinetic parameters from the evolution of pixels intensities in 3D+t acquisitions. However, this requires a registration of the whole sequence of volumes, which is challenging especially when the patient breathes freely. In this paper, we propose a generic, fast and automatic method to address this problem.

Incorporating shape variability in image segmentation via implicit template deformation.

Implicit template deformation is a model-based segmentation framework that was successfully applied in several medical applications. In this paper, we propose a method to learn and use prior knowledge on shape variability in such framework. This shape prior is learnt via an original and dedicated process in which both an optimal template and principal modes of variations are estimated from a collection of shapes.

Rapid assessment of liver volumetry by a novel automated segmentation algorithm.

Objective: This study aimed to evaluate a novel segmentation software for automated liver volumetry and segmentation regarding segmentation speed and interobserver variability.

Methods: Computed tomographic scans of 20 patients without underlying liver disease and 10 patients with liver metastasis from colorectal cancer were analyzed by a novel segmentation software. Liver segmentation was performed after manual placement of specific landmarks into 9 segments according to the Couinaud model as well as into 4 segments, the latter being import for surgery planning.

Semiautomatic volumetric tumor segmentation for hepatocellular carcinoma: comparison between C-arm cone beam computed tomography and MRI.

Rationale And Objectives: To evaluate the precision and reproducibility of a semiautomatic tumor segmentation software in measuring tumor volume of hepatocellular carcinoma (HCC) before the first transarterial chemo-embolization (TACE) on contrast-enhancement magnetic resonance imaging (CE-MRI) and intraprocedural dual-phase C-arm cone beam computed tomography (DP-CBCT) images.

Materials And Methods: Nineteen HCCs were targeted in 19 patients (one per patient) who underwent baseline diagnostic CE-MRI and an intraprocedural DP-CBCT. The images were obtained from CE-MRI (arterial phase of an intravenous contrast medium injection) and DP-CBCT (delayed phase of an intra-arterial contrast medium injection) before the actual embolization.

Automatic detection and segmentation of kidneys in 3D CT images using random forests.

Kidney segmentation in 3D CT images allows extracting useful information for nephrologists. For practical use in clinical routine, such an algorithm should be fast, automatic and robust to contrast-agent enhancement and fields of view. By combining and refining state-of-the-art techniques (random forests and template deformation), we demonstrate the possibility of building an algorithm that meets these requirements.

Real-time 3D image segmentation by user-constrained template deformation.

We describe an algorithm for 3D interactive image segmentation by non-rigid implicit template deformation, with two main original features. First, our formulation incorporates user input as inside/outside labeled points to drive the deformation and improve both robustness and accuracy. This yields inequality constraints, solved using an Augmented Lagrangian approach.

Joint co-segmentation and registration of 3D ultrasound images.

Contrast-enhanced ultrasound (CEUS) allows a visualization of the vascularization and complements the anatomical information provided by conventional ultrasound (US). However, these images are inherently subject to noise and shadows, which hinders standard segmentation algorithms. In this paper, we propose to use simultaneously the different information coming from 3D US and CEUS images to address the problem of kidney segmentation.

Quantitative and volumetric European Association for the Study of the Liver and Response Evaluation Criteria in Solid Tumors measurements: feasibility of a semiautomated software method to assess tumor response after transcatheter arterial chemoembolization.

Purpose: To show that hepatic tumor volume and enhancement pattern measurements can be obtained in a time-efficient and reproducible manner on a voxel-by-voxel basis to provide a true three-dimensional (3D) volumetric assessment.

Materials And Methods: Magnetic resonance (MR) imaging data obtained from 20 patients recruited for a single-institution prospective study were retrospectively evaluated. All patients had a diagnosis of hepatocellular carcinoma (HCC) and underwent drug-eluting beads (DEB) transcatheter arterial chemoembolization for the first time.

Comparison of semi-automatic volumetric VX2 hepatic tumor segmentation from cone beam CT and multi-detector CT with histology in rabbit models.

Rationale And Objectives: The purpose of this study was to compare tumor volume in a VX2 rabbit model as calculated using semiautomatic tumor segmentation from C-arm cone-beam computed tomography (CBCT) and multidetector computed tomography (MDCT) to the actual tumor volume.

Materials And Methods: Twenty VX2 tumors in 20 adult male New Zealand rabbits (one tumor per rabbit) were imaged with CBCT (using an intra-arterial contrast medium injection) and MDCT (using an intravenous contrast injection). All tumor volumes were measured using semiautomatic three-dimensional volumetric segmentation software.