Deep learning applications in chest radiography and computed tomography: current state of the art
Deep learning is a genre of machine learning that allows computational models to learn
representations of data with multiple levels of abstraction using numerous processing layers …
representations of data with multiple levels of abstraction using numerous processing layers …
Abdomenct-1k: Is abdominal organ segmentation a solved problem?
With the unprecedented developments in deep learning, automatic segmentation of main
abdominal organs seems to be a solved problem as state-of-the-art (SOTA) methods have …
abdominal organs seems to be a solved problem as state-of-the-art (SOTA) methods have …
A systematic review of automated segmentation methods and public datasets for the lung and its lobes and findings on computed tomography images
Objectives: Automated computational segmentation of the lung and its lobes and findings in
X-Ray based computed tomography (CT) images is a challenging problem with important …
X-Ray based computed tomography (CT) images is a challenging problem with important …
DeepTarget: Gross tumor and clinical target volume segmentation in esophageal cancer radiotherapy
Gross tumor volume (GTV) and clinical target volume (CTV) delineation are two critical steps
in the cancer radiotherapy planning. GTV defines the primary treatment area of the gross …
in the cancer radiotherapy planning. GTV defines the primary treatment area of the gross …
Fully automated lung lobe segmentation in volumetric chest CT with 3D U-Net: validation with intra-and extra-datasets
Lung lobe segmentation in chest CT has been used for the analysis of lung functions and
surgical planning. However, accurate lobe segmentation is difficult as 80% of patients have …
surgical planning. However, accurate lobe segmentation is difficult as 80% of patients have …
CT image segmentation for inflamed and fibrotic lungs using a multi-resolution convolutional neural network
The purpose of this study was to develop a fully-automated segmentation algorithm, robust
to various density enhancing lung abnormalities, to facilitate rapid quantitative analysis of …
to various density enhancing lung abnormalities, to facilitate rapid quantitative analysis of …
Lymphocyte-driven regional immunopathology in pneumonitis caused by impaired central immune tolerance
EMN Ferré, TJ Break, PD Burbelo, M Allgäuer… - Science translational …, 2019 - science.org
Autoimmune polyendocrinopathy-candidiasis-ectodermal dystrophy (APECED), a
monogenic disorder caused by AIRE mutations, presents with several autoimmune …
monogenic disorder caused by AIRE mutations, presents with several autoimmune …
Artificial intelligence in radiology
The interest in artificial intelligence (AI) has ballooned within radiology in the past few years
primarily due to notable successes of deep learning. With the advances brought by deep …
primarily due to notable successes of deep learning. With the advances brought by deep …
End-to-end supervised lung lobe segmentation
The segmentation and characterization of the lung lobes are important tasks for Computer
Aided Diagnosis (CAD) systems related to pulmonary disease. The detection of the fissures …
Aided Diagnosis (CAD) systems related to pulmonary disease. The detection of the fissures …
A fully automatic segmentation pipeline of pulmonary lobes before and after lobectomy from computed tomography images
Background and objective Lobectomy is a curative treatment for localized lung cancer. The
study aims to construct an automatic pipeline for segmenting pulmonary lobes before and …
study aims to construct an automatic pipeline for segmenting pulmonary lobes before and …