Semiautomated quantification of the fibrous tissue response to complex three‐dimensional filamentous scaffolds using digital image analysis

Fibrosis represents a relevant response to the implantation of biomaterials, which occurs not only at the tissue-material interface (fibrotic encapsulation) but also within the void fraction of complex three-dimensional (3D) biomaterial constructions (fibrotic ingrowth). Usual evaluation of the biocompatibility mostly depicts fibrosis at the interface of the biomaterial using semiquantitative scores. Here, the relations between encapsulation and infiltrating fibrotic growth are poorly represented. Virtual pathology and digital image analysis provide new strategies to assess fibrosis in a more differentiated way. In this study, we adopted a method previously used to quantify fibrosis in visc…

Vollautomatische, lappenbasierte Segmentierung von MR-Pefusionsmessungen in COPD Patienten mit Methoden des maschinellen Lernens

LATE-BREAKING ABSTRACT: Comparative studies on bronchuswall-thickness by histologic and computed tomographic measurements of porcine lungs

Aims: Histologic slides are commonly used as template in the evaluation and development of medical imaging methods.Diseases like Asthma and COPD show characteristic changes in airway morphology and airway measurement by computed tomography is a promising diagnostic approach.However,shrinkage caused by fixation and histological processing is known in lung tissue.In this study,the thickness of bronchus walls in paraffin and frozen sections as well as in CT and MicroCT were compared. Methods: Airway measurements of swine lungs were performed after freezing in ventilated condition in liquid nitrogen by measuring the wall thickness of 7 bronchi via CT and MicroCT as well as in frozen and paraffi…

A 3D Deep Neural Network for Liver Volumetry in 3T Contrast-Enhanced MRI.

 To create a fully automated, reliable, and fast segmentation tool for Gd-EOB-DTPA-enhanced MRI scans using deep learning. Datasets of Gd-EOB-DTPA-enhanced liver MR images of 100 patients were assembled. Ground truth segmentation of the hepatobiliary phase images was performed manually. Automatic image segmentation was achieved with a deep convolutional neural network. Our neural network achieves an intraclass correlation coefficient (ICC) of 0.987, a Sørensen-Dice coefficient of 96.7 ± 1.9 % (mean ± std), an overlap of 92 ± 3.5 %, and a Hausdorff distance of 24.9 ± 14.7 mm compared with two expert readers who corresponded to an ICC of 0.973, a Sørensen-Dice coefficient of 95.2 ± 2.8 %, and…

Deep semantic lung segmentation for tracking potential pulmonary perfusion biomarkers in chronic obstructive pulmonary disease (COPD): The multi‐ethnic study of atherosclerosis COPD study

Background Chronic obstructive pulmonary disease (COPD) is associated with high morbidity and mortality. Identification of imaging biomarkers for phenotyping is necessary for future treatment and therapy monitoring. However, translation of visual analytic pipelines into clinics or their use in large-scale studies is significantly slowed by time-consuming postprocessing steps. Purpose To implement an automated tool chain for regional quantification of pulmonary microvascular blood flow in order to reduce analysis time and user variability. Study type Prospective. Population In all, 90 MRI scans of 63 patients, of which 31 had a COPD with a mean Global Initiative for Chronic Obstructive Lung …