Magnetic Resonance Imaging-Based Delineation of Organs at Risk in the Head and Neck Region.

Purpose: The aim of this article is to establish a comprehensive contouring guideline for treatment planning using only magnetic resonance images through an up-to-date set of organs at risk (OARs), recommended organ boundaries, and relevant suggestions for the magnetic resonance imaging (MRI)-based delineation of OARs in the head and neck (H&N) region.

Methods And Materials: After a detailed review of the literature, MRI data were collected from the H&N region of healthy volunteers. OARs were delineated in the axial, coronal, and sagittal planes on T2-weighted sequences.

Multi-modal trained artificial intelligence solution to triage chest X-ray for COVID-19 using pristine ground-truth, versus radiologists.

The front-line imaging modalities computed tomography (CT) and X-ray play important roles for triaging COVID patients. Thoracic CT has been accepted to have higher sensitivity than a chest X-ray for COVID diagnosis. Considering the limited access to resources (both hardware and trained personnel) and issues related to decontamination, CT may not be ideal for triaging suspected subjects.

Subventricular zone volumetric and dosimetric changes during postoperative brain tumor irradiation and its impact on overall survival.

Purpose: The aim of this retrospective study was to investigate the relationship between the dose to the subventricular zone (SVZ) and overall survival (OS) of 41 patients with glioblastoma multiforme (GBM), who were treated with an adaptive approach involving repeated topometric CT and replanning at two-thirds (40 Gy) of their course of postoperative radiotherapy for planning of a 20 Gy boost.

Methods: We examined changes in the ipsilateral lateral ventricle (LV) and SVZ (iLV and iSVZ), as well as in the contralateral LV and SVZ (cLV and cSVZ). We evaluated the volumetric changes on both planning CT scans (primary CT1 and secondary CT2).

Automatic recognition of anatomical regions in three-dimensional medical images.

This paper presents a method that detects anatomy regions in three-dimensional medical images. The method labels each axial slice of the image according to the anatomy region it belongs to. The detected regions are the head (and neck), the chest, the abdomen, the pelvis, and the legs.

Automated liver lesion detection in CT images based on multi-level geometric features.

Purpose: Due to the increasing number of liver cancer cases in clinical practice, there is a significant need for efficient tools for computer-assisted liver lesion analysis. A wide range of clinical applications, such as lesion characterization, quantification and follow-up, can be facilitated by automated liver lesion detection. Liver lesions vary significantly in size, shape, density and heterogeneity, which make them difficult to detect automatically.

Virtual volume resection using multi-resolution triangular representation of B-spline surfaces.

Computer assisted analysis of organs has an important role in clinical diagnosis and therapy planning. As well as the visualization, the manipulation of 3-dimensional (3D) objects are key features of medical image processing tools. The goal of this work was to develop an efficient and easy to use tool that allows the physician to partition a segmented organ into its segments or lobes.

Automated liver volumetry in orthotopic liver transplantation using multiphase acquisitions on MDCT.

Objective: The aim of this study was to test a new automated hepatic volumetry technique by comparing the accuracies and postprocessing times of manual and automated liver volume segmentation methods in a patient population undergoing orthotopic liver transplantation so that liver volume could be determined on pathology as the standard of reference.

Conclusion: Both manual and automated multiphase MDCT-based volume measurements were strongly correlated to liver volume (Pearson correlation coefficient, r = 0.87 [p < 0.

Liver segmentation for contrast-enhanced MR images using partitioned probabilistic model.

Purpose: Liver volume segmentation is important in computer assisted diagnosis and therapy planning of liver tumors. Manual segmentation is time-consuming, tedious and error prone, so automated methods are needed. Automatic segmentation of MR images is more challenging than for CT images, so a robust system was developed.

Automatic segmentation of the liver from multi- and single-phase contrast-enhanced CT images.

Segmentation of contrast-enhanced abdominal CT images is required by many clinical applications of computer aided diagnosis and therapy planning. The research on automated methods involves different organs among which the liver is the most emphasized. In the current clinical practice more images (at different phases) are acquired from the region of interest in case of a contrast-enhanced abdominal CT examination.

Comparison and evaluation of methods for liver segmentation from CT datasets.

This paper presents a comparison study between 10 automatic and six interactive methods for liver segmentation from contrast-enhanced CT images. It is based on results from the "MICCAI 2007 Grand Challenge" workshop, where 16 teams evaluated their algorithms on a common database. A collection of 20 clinical images with reference segmentations was provided to train and tune algorithms in advance.