Computer-aided detection of bladder tumors based on the thickness mapping of bladder wall in MR images

2010 ◽  
Author(s):  
Hongbin Zhu ◽  
Chaijie Duan ◽  
Ruirui Jiang ◽  
Lihong Li ◽  
Yi Fan ◽  
...  
Keyword(s):  
Bladder Wall ◽  
Computer Aided Detection ◽  
Mr Images ◽  
Bladder Tumors ◽  
Computer Aided

Computer-aided detection of bladder wall thickening in CT urography (CTU)

2018 ◽  
Author(s):  
Kenny H. Cha ◽  
Lubomir M. Hadjiiski ◽  
Heang-Ping Chan ◽  
Elaine M. Caoili ◽  
Richard H. Cohan ◽  
...  
Keyword(s):  
Bladder Wall ◽  
Wall Thickening ◽  
Ct Urography ◽  
Computer Aided Detection ◽  
Computer Aided

scholarly journals Computer-aided detection of radiation-induced cerebral microbleeds on susceptibility-weighted MR images

2013 ◽  
Vol 2 ◽  
pp. 282-290 ◽  
Author(s):  
Wei Bian ◽  
Christopher P. Hess ◽  
Susan M. Chang ◽  
Sarah J. Nelson ◽  
Janine M. Lupo
Keyword(s):  
Cerebral Microbleeds ◽  
Computer Aided Detection ◽  
Mr Images ◽  
Computer Aided ◽  
Radiation Induced

scholarly journals Deep Learning-Based Computer-Aided Detection System for Automated Treatment Response Assessment of Brain Metastases on 3D MRI

2021 ◽  
Vol 11 ◽  
Author(s):  
Jungheum Cho ◽  
Young Jae Kim ◽  
Leonard Sunwoo ◽  
Gi Pyo Lee ◽  
Toan Quang Nguyen ◽  
...  
Keyword(s):  
Deep Learning ◽  
Brain Metastases ◽  
Treatment Response ◽  
Response Assessment ◽  
Computer Aided Detection ◽  
Mr Images ◽  
Test Set ◽  
One Dimensional ◽  
Computer Aided ◽  
Treatment Response Assessment

BackgroundAlthough accurate treatment response assessment for brain metastases (BMs) is crucial, it is highly labor intensive. This retrospective study aimed to develop a computer-aided detection (CAD) system for automated BM detection and treatment response evaluation using deep learning.MethodsWe included 214 consecutive MRI examinations of 147 patients with BM obtained between January 2015 and August 2016. These were divided into the training (174 MR images from 127 patients) and test datasets according to temporal separation (temporal test set #1; 40 MR images from 20 patients). For external validation, 24 patients with BM and 11 patients without BM from other institutions were included (geographic test set). In addition, we included 12 MRIs from BM patients obtained between August 2017 and March 2020 (temporal test set #2). Detection sensitivity, dice similarity coefficient (DSC) for segmentation, and agreements in one-dimensional and volumetric Response Assessment in Neuro-Oncology Brain Metastases (RANO-BM) criteria between CAD and radiologists were assessed.ResultsIn the temporal test set #1, the sensitivity was 75.1% (95% confidence interval [CI]: 69.6%, 79.9%), mean DSC was 0.69 ± 0.22, and false-positive (FP) rate per scan was 0.8 for BM ≥ 5 mm. Agreements in the RANO-BM criteria were moderate (κ, 0.52) and substantial (κ, 0.68) for one-dimensional and volumetric, respectively. In the geographic test set, sensitivity was 87.7% (95% CI: 77.2%, 94.5%), mean DSC was 0.68 ± 0.20, and FP rate per scan was 1.9 for BM ≥ 5 mm. In the temporal test set #2, sensitivity was 94.7% (95% CI: 74.0%, 99.9%), mean DSC was 0.82 ± 0.20, and FP per scan was 0.5 (6/12) for BM ≥ 5 mm.ConclusionsOur CAD showed potential for automated treatment response assessment of BM ≥ 5 mm.

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