Lee, Scott J Weinberg, Brent D Gore, Ashwani Banerjee, Imon
Published in
Journal of digital imaging
The aim of this study is to develop an automated classification method for Brain Tumor Reporting and Data System (BT-RADS) categories from unstructured and structured brain magnetic resonance imaging (MR) reports. This retrospective study included 1410 BT-RADS structured reports dated from January 2014 to December 2017 and a test set of 109 unstruc...
Cristofaro, Massimo Piselli, Pierluca Pianura, Elisa Petrone, Ada Cimaglia, Claudia Di Stefano, Federica Albarello, Fabrizio Schininà, Vincenzo
Published in
Journal of digital imaging
To assess the incidence of outpatient examinations delivered through a web portal in the Latium Region in 2 years and compare socio-demographic characteristics of these users compared to the total of examinations performed. All radiological exams (including MRI, X-ray and CT) performed from March 2017 to February 2019 were retrospectively analysed....
Eichelberg, Marco Kleber, Klaus Kämmerer, Marc
Published in
Journal of digital imaging
This article provides an overview on the literature published on the topic of cybersecurity for PACS (Picture Archiving and Communications Systems) and medical imaging. From a practical perspective, PACS specific security measures must be implemented together with the measures applicable to the IT infrastructure as a whole, in order to prevent inci...
Nebbia, Giacomo Zhang, Qian Arefan, Dooman Zhao, Xinxiang Wu, Shandong
Published in
Journal of digital imaging
Microvascular invasion (mVI) is the most significant independent predictor of recurrence for hepatocellular carcinoma (HCC), but its pre-operative assessment is challenging. In this study, we investigate the use of multi-parametric MRI radiomics to predict mVI status before surgery. We retrospectively collected pre-operative multi-parametric liver ...
Stadler, Caroline Bivik Lindvall, Martin Lundström, Claes Bodén, Anna Lindman, Karin Rose, Jeronimo Treanor, Darren Blomma, Johan Stacke, Karin Pinchaud, Nicolas
...
Published in
Journal of digital imaging
Artificial intelligence (AI) holds much promise for enabling highly desired imaging diagnostics improvements. One of the most limiting bottlenecks for the development of useful clinical-grade AI models is the lack of training data. One aspect is the large amount of cases needed and another is the necessity of high-quality ground truth annotation. T...
Liu, Caixia Pang, Mingyong
Published in
Journal of digital imaging
Lung segmentation is a key step of thoracic computed tomography (CT) image processing, and it plays an important role in computer-aided pulmonary disease diagnostics. However, the presence of image noises, pathologies, vessels, individual anatomical varieties, and so on makes lung segmentation a complex task. In this paper, we present a fully autom...
de Moura, Joaquim Samagaio, Gabriela Novo, Jorge Almuina, Pablo Fernández, María Isabel Ortega, Marcos
Published in
Journal of digital imaging
The automatic identification and segmentation of edemas associated with diabetic macular edema (DME) constitutes a crucial ophthalmological issue as they provide useful information for the evaluation of the disease severity. According to clinical knowledge, the DME disorder can be categorized into three main pathological types: serous retinal detac...
Narisawa, Chiaki Sutherland, Kenneth Lu, Yutong Furusaki, Akira Sagawa, Akira Kamishima, Tamotsu
Published in
Journal of digital imaging
In rheumatoid arthritis (RA), the radiographic progression of joint space narrowing (JSN) is evaluated using visual assessments. However, those methods are complicated and time-consuming. We developed an automatic system that can detect joint locations and compute the joint space difference index (JSDI), which was defined as the chronological chang...
Rahman, Md Asadur Siddik, Abu Bakar Ghosh, Tarun Kanti Khanam, Farzana Ahmad, Mohiuddin
Published in
Journal of digital imaging
Functional near-infrared spectroscopy (fNIRS) is a relatively new imaging modality in the functional neuroimaging research arena. The fNIRS modality non-invasively investigates the change of blood oxygenation level in the human brain utilizing the transillumination technique. In the last two decades, the interest in this modality is gradually evolv...
Lidén, Mats Hjelmgren, Ola Vikgren, Jenny Thunberg, Per
Published in
Journal of digital imaging
Emphysema is visible on computed tomography (CT) as low-density lesions representing the destruction of the pulmonary alveoli. To train a machine learning model on the emphysema extent in CT images, labeled image data is needed. The provision of these labels requires trained readers, who are a limited resource. The purpose of the study was to test ...