scholarly journals A Deep Learning Automated Segmentation Algorithm Accurately Detects Differences in Longitudinal Cartilage Thickness Loss – Data from the FNIH Biomarkers Study of the Osteoarthritis Initiative

2020 ◽  
Author(s):  
Felix Eckstein ◽  
Akshay S. Chaudhari ◽  
David Fuerst ◽  
Martin Gaisberger ◽  
Jana Kemnitz ◽  
...  
Keyword(s):  
Deep Learning ◽  
Cartilage Thickness ◽  
Segmentation Algorithm ◽  
Automated Segmentation ◽  
Thickness Loss ◽  
Loss Data ◽  
Osteoarthritis Initiative

scholarly journals Precision, Reliability, and Responsiveness of a Novel Automated Quantification Tool for Cartilage Thickness: Data from the Osteoarthritis Initiative

2019 ◽  
Vol 47 (2) ◽  
pp. 282-289 ◽  
Author(s):  
Michael Antony Bowes ◽  
Gwenael Alain Guillard ◽  
Graham Richard Vincent ◽  
Alan Donald Brett ◽  
Christopher Brian Hartley Wolstenholme ◽  
...  
Keyword(s):  
Epidemiological Studies ◽  
Magnetic Resonance Images ◽  
Cartilage Thickness ◽  
Systematic Bias ◽  
Automated Segmentation ◽  
Manual Segmentation ◽  
Training Set ◽  
Set Agreement ◽  
Osteoarthritis Initiative ◽  
Automated Technique

Objective.Accurate automated segmentation of cartilage should provide rapid reliable outcomes for both epidemiological studies and clinical trials. We aimed to assess the precision and responsiveness of cartilage thickness measured with careful manual segmentation or a novel automated technique.Methods.Agreement of automated segmentation was assessed against 2 manual segmentation datasets: 379 magnetic resonance images manually segmented in-house (training set), and 582 from the Osteoarthritis Initiative with data available at 0, 1, and 2 years (biomarkers set). Agreement of mean thickness was assessed using Bland-Altman plots, and change with pairwise Student t test in the central medial femur (cMF) and tibia regions (cMT). Repeatability was assessed on a set of 19 knees imaged twice on the same day. Responsiveness was assessed using standardized response means (SRM).Results.Agreement of manual versus automated methods was excellent with no meaningful systematic bias (training set: cMF bias 0.1 mm, 95% CI ± 0.35; biomarkers set: bias 0.1 mm ± 0.4). The smallest detectable difference for cMF was 0.13 mm (coefficient of variation 3.1%), and for cMT 0.16 mm(2.65%). Reported change using manual segmentations in the cMF region at 1 year was −0.031 mm (95% CI −0.022, −0.039), p < 10−4, SRM −0.31 (−0.23, −0.38); and at 2 years was −0.071 (−0.058, −0.085), p < 10−4, SRM −0.43 (−0.36, −0.49). Reported change using automated segmentations in the cMF at 1 year was −0.059 (−0.047, −0.071), p < 10−4, SRM −0.41 (−0.34, −0.48); and at 2 years was −0.14 (−0.123, −0.157, p < 10−4, SRM −0.67 (−0.6, −0.72).Conclusion.A novel cartilage segmentation method provides highly accurate and repeatable measures with cartilage thickness measurements comparable to those of careful manual segmentation, but with improved responsiveness.

scholarly journals Rates and sensitivity of knee cartilage thickness loss in specific central reading radiographic strata from the osteoarthritis initiative

2014 ◽  
Vol 22 (10) ◽  
pp. 1550-1553 ◽  
Author(s):  
S. Maschek ◽  
W. Wirth ◽  
C. Ladel ◽  
M.-P. Hellio Le Graverand ◽  
F. Eckstein
Keyword(s):  
Cartilage Thickness ◽  
Knee Cartilage ◽  
Thickness Loss ◽  
Osteoarthritis Initiative

scholarly journals Automated Segmentation of Infarct Lesions in T1-Weighted MRI Scans Using Variational Mode Decomposition and Deep Learning

Sensors ◽  
2021 ◽  
Vol 21 (6) ◽  
pp. 1952
Author(s):  
May Phu Paing ◽  
Supan Tungjitkusolmun ◽  
Toan Huy Bui ◽  
Sarinporn Visitsattapongse ◽  
Chuchart Pintavirooj
Keyword(s):  
Deep Learning ◽  
Brain Infarction ◽  
Three Dimensional ◽  
Dice Similarity Coefficient ◽  
Automated Segmentation ◽  
Variational Mode Decomposition ◽  
Brain Scans ◽  
Automated Method ◽  
Mode Decomposition ◽  
Segmentation Task

Automated segmentation methods are critical for early detection, prompt actions, and immediate treatments in reducing disability and death risks of brain infarction. This paper aims to develop a fully automated method to segment the infarct lesions from T1-weighted brain scans. As a key novelty, the proposed method combines variational mode decomposition and deep learning-based segmentation to take advantages of both methods and provide better results. There are three main technical contributions in this paper. First, variational mode decomposition is applied as a pre-processing to discriminate the infarct lesions from unwanted non-infarct tissues. Second, overlapped patches strategy is proposed to reduce the workload of the deep-learning-based segmentation task. Finally, a three-dimensional U-Net model is developed to perform patch-wise segmentation of infarct lesions. A total of 239 brain scans from a public dataset is utilized to develop and evaluate the proposed method. Empirical results reveal that the proposed automated segmentation can provide promising performances with an average dice similarity coefficient (DSC) of 0.6684, intersection over union (IoU) of 0.5022, and average symmetric surface distance (ASSD) of 0.3932, respectively.

scholarly journals Diagnosing osteoarthritis from T2 maps using deep learning: an analysis of the entire Osteoarthritis Initiative baseline cohort

2019 ◽  
Vol 27 (7) ◽  
pp. 1002-1010 ◽  
Author(s):  
V. Pedoia ◽  
J. Lee ◽  
B. Norman ◽  
T.M. Link ◽  
S. Majumdar
Keyword(s):  
Deep Learning ◽  
Osteoarthritis Initiative
Sign in / Sign up

Export Citation Format

Share Document