A deep-learning semantic segmentation approach to fully automated MRI-based left-ventricular deformation analysis in cardiotoxicity

2021 ◽  
Author(s):  
By Julia Kar ◽  
Michael V. Cohen ◽  
Samuel P. McQuiston ◽  
Christopher M. Malozzi
Keyword(s):  
Deep Learning ◽  
Semantic Segmentation ◽  
Left Ventricular ◽  
Deformation Analysis ◽  
Left Ventricular Deformation ◽  
Segmentation Approach

Abstract 224: A Deep Learning Approach to Left-Ventricular Chamber Quantification for Fully Automated Three Dimensional Strain Analysis in Cardiotoxicity

2020 ◽  
Vol 127 (Suppl_1) ◽  
Author(s):  
Bryant M Baldwin ◽  
Shane Joseph ◽  
Xiaodong Zhong ◽  
Ranya Kakish ◽  
Cherie Revere ◽  
...  
Keyword(s):  
Deep Learning ◽  
Interpolation Method ◽  
Three Dimensional ◽  
Strain Analysis ◽  
Correlation Coefficients ◽  
Semantic Segmentation ◽  
Left Ventricular ◽  
Breast Cancer Patients ◽  
Left Ventricular Chamber ◽  
Longitudinal Strains

This study investigated MRI and semantic segmentation-based deep-learning (SSDL) automation for left-ventricular chamber quantifications (LVCQ) and low longitudinal strain (LLS) determination, thus eliminating user-bias by providing an automated tool to detect cardiotoxicity (CT) in breast cancer patients treated with antineoplastic agents. Displacement Encoding with Stimulated Echoes-based (DENSE) myocardial images from 26 patients were analyzed with the tool’s Convolution Neural Network with underlying Resnet-50 architecture. Quantifications based on the SSDL tool’s output were for LV end-diastolic diameter (LVEDD), ejection fraction (LVEF), and mass (LVM) (see figure for phase sequence). LLS was analyzed with Radial Point Interpolation Method (RPIM) with DENSE phase-based displacements. LVCQs were validated by comparison to measurements obtained with an existing semi-automated vendor tool (VT) and strains by 2 independent users employing Bland-Altman analysis (BAA) and interclass correlation coefficients estimated with Cronbach’s Alpha (C-Alpha) index. F1 score for classification accuracy was 0.92. LVCQs determined by SSDL and VT were 4.6 ± 0.5 vs 4.6 ± 0.7 cm (C-Alpha = 0.93 and BAA = 0.5 ± 0.5 cm) for LVEDD, 58 ± 5 vs 58 ± 6 % (0.90, 1 ± 5%) for LVEF, 119 ± 17 vs 121 ± 14 g (0.93, 5 ± 8 g) for LV mass, while LLS was 14 ± 4 vs 14 ± 3 % (0.86, 0.2 ± 6%). Hence, equivalent LV dimensions, mass and strains measured by VT and DENSE imaging validate our unique automated analytic tool. Longitudinal strains in patients can then be analyzed without user bias to detect abnormalities for the indication of cardiotoxicity and the need for therapeutic intervention even if LVEF is not affected.

Two-dimensional and three-dimensional left ventricular deformation analysis: a study in competitive athletes

2016 ◽  
Vol 32 (12) ◽  
pp. 1697-1705 ◽  
Author(s):  
Flavio D’Ascenzi ◽  
Marco Solari ◽  
Michele Mazzolai ◽  
Matteo Cameli ◽  
Matteo Lisi ◽  
...  
Keyword(s):  
Three Dimensional ◽  
Left Ventricular ◽  
Deformation Analysis ◽  
Two Dimensional ◽  
Left Ventricular Deformation ◽  
Competitive Athletes

scholarly journals The semantic segmentation approach for normal and pathologic tympanic membrane using deep learning

2019 ◽  
Author(s):  
Jungirl Seok ◽  
Jae-Jin Song ◽  
Ja-Won Koo ◽  
Hee Chan Kim ◽  
Byung Yoon Choi
Keyword(s):  
Deep Learning ◽  
Tympanic Membrane ◽  
Medical Records ◽  
Semantic Segmentation ◽  
Detection Accuracy ◽  
Test Set ◽  
Starting Point ◽  
Segmentation Approach ◽  
Validation Set ◽  
Deep Learning Model

AbstractObjectivesThe purpose of this study was to create a deep learning model for the detection and segmentation of major structures of the tympanic membrane.MethodsTotal 920 tympanic endoscopic images had been stored were obtained, retrospectively. We constructed a detection and segmentation model using Mask R-CNN with ResNet-50 backbone targeting three clinically meaningful structures: (1) tympanic membrane (TM); (2) malleus with side of tympanic membrane; and (3) suspected perforation area. The images were randomly divided into three sets – taining set, validation set, and test set – at a ratio of 0.6:0.2:0.2, resulting in 548, 187, and 185 images, respectively. After assignment, 548 tympanic membrane images were augmented 50 times each, reaching 27,400 images.ResultsAt the most optimized point of the model, it achieved a mean average precision of 92.9% on test set. When an intersection over Union (IoU) score of greater than 0.5 was used as the reference point, the tympanic membrane was 100% detectable, the accuracy of side of the tympanic membrane based on the malleus segmentation was 88.6% and detection accuracy of suspicious perforation was 91.4%.ConclusionsAnatomical segmentation may allow the inclusion of an explanation provided by deep learning as part of the results. This method is applicable not only to tympanic endoscope, but also to sinus endoscope, laryngoscope, and stroboscope. Finally, it will be the starting point for the development of automated medical records descriptor of endoscope images.

Abstract P345: A MRI Displacement-based Deep-learning Semantic Segmentation Tool For Left-ventricular Longitudinal Strain Analysis In Cardiotoxicity

2021 ◽  
Vol 129 (Suppl_1) ◽  
Author(s):  
Julia Kar ◽  
Michael V Cohen ◽  
Teja Poorsala ◽  
Samuel A McQuiston ◽  
Cheri Revere ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Deep Learning ◽  
Cancer Patients ◽  
Healthy Subjects ◽  
Longitudinal Strain ◽  
Strain Analysis ◽  
Semantic Segmentation ◽  
Left Ventricular ◽  
Phase Unwrapping ◽  
Breast Cancer Patients

Global longitudinal strain (GLS) computed in the left-ventricle (LV) is an established metric for detecting cardiotoxicity in breast cancer patients treated with antineoplastic agents. The purpose of this study was to develop a novel, MRI-based, deep-learning semantic segmentation tool that automates the phase-unwrapping for LV displacement computation in GLS. Strain analysis via phase-unwrapping was conducted on 30 breast cancer patients and 30 healthy females acquired with the Displacement Encoding with Stimulated Echoes (DENSE) sequence. A ResNet-50 deep convolutional neural network (DCNN) architecture for automated phase-unwrapping, a previously validated ResNet-50 DCNN for chamber quantification and the Radial Point Interpolation Method were used for GLS computation (Figure 1). The DCNN's performance was measured with F1 and Dice scores, and validated in comparison to the robust transport of intensity equation (RTIE) and quality guided phase-unwrapping (QGPU) conventional algorithms. The three techniques were compared by intraclass correlation coefficient with Cronbach’s alpha (C-alpha) index. Classification accuracy with the DCNN was F1 score of 0.92 and Dice score of 0.89. The GLS results from RTIE, QGPU and DCNN were -16.0 ± 2%, -16.1 ± 3% and -15.9 ± 3% (C-alpha = 0.89) for patients and -18.9 ± 3%, -19.0 ± 4% and -18.9 ± 3% (C-alpha = 0.92) for healthy subjects. Comparable validation results from the three techniques show the feasibility of a DCNN-based approach to LV displacement and GLS analysis. The dissimilarities between patients and healthy subjects demonstrate that DCNN-based GLS computation may detect LV abnormalities related to cardiotoxicity.

Supervoxel Convolution for Online 3D Semantic Segmentation

2021 ◽  
Vol 40 (3) ◽  
pp. 1-15
Author(s):  
Shi-Sheng Huang ◽  
Ze-Yu Ma ◽  
Tai-Jiang Mu ◽  
Hongbo Fu ◽  
Shi-Min Hu
Keyword(s):  
Deep Learning ◽  
Semantic Segmentation ◽  
Compact Representation ◽  
Joint Learning ◽  
The Public ◽  
Segmentation Accuracy ◽  
Segmentation Approach ◽  
3D Scene ◽  
3D Scene Reconstruction ◽  
3D Surfaces

Online 3D semantic segmentation, which aims to perform real-time 3D scene reconstruction along with semantic segmentation, is an important but challenging topic. A key challenge is to strike a balance between efficiency and segmentation accuracy. There are very few deep-learning-based solutions to this problem, since the commonly used deep representations based on volumetric-grids or points do not provide efficient 3D representation and organization structure for online segmentation. Observing that on-surface supervoxels, i.e., clusters of on-surface voxels, provide a compact representation of 3D surfaces and brings efficient connectivity structure via supervoxel clustering, we explore a supervoxel-based deep learning solution for this task. To this end, we contribute a novel convolution operation (SVConv) directly on supervoxels. SVConv can efficiently fuse the multi-view 2D features and 3D features projected on supervoxels during the online 3D reconstruction, and leads to an effective supervoxel-based convolutional neural network, termed as Supervoxel-CNN , enabling 2D-3D joint learning for 3D semantic prediction. With the Supervoxel-CNN , we propose a clustering-then-prediction online 3D semantic segmentation approach. The extensive evaluations on the public 3D indoor scene datasets show that our approach significantly outperforms the existing online semantic segmentation systems in terms of efficiency or accuracy.

Validation of a deep-learning semantic segmentation approach to fully automate MRI-based left-ventricular deformation analysis in cardiotoxicity

2021 ◽  
Vol 94 (1120) ◽  
pp. 20201101
Author(s):  
Julia Karr ◽  
Michael Cohen ◽  
Samuel A McQuiston ◽  
Teja Poorsala ◽  
Christopher Malozzi
Keyword(s):  
Breast Cancer ◽  
Deep Learning ◽  
Repeated Measures ◽  
Performance Metrics ◽  
Intraclass Correlation ◽  
Myocardial Strain ◽  
Strain Analysis ◽  
Left Ventricular ◽  
Cancer Data ◽  
Left Ventricular Deformation

Objective: Left-ventricular (LV) strain measurements with the Displacement Encoding with Stimulated Echoes (DENSE) MRI sequence provide accurate estimates of cardiotoxicity damage related to chemotherapy for breast cancer. This study investigated an automated and supervised deep convolutional neural network (DCNN) model for LV chamber quantification before strain analysis in DENSE images. Methods: The DeepLabV3 +DCNN with three versions of ResNet-50 backbone was designed to conduct chamber quantification on 42 female breast cancer data sets. The convolutional layers in the three ResNet-50 backbones were varied as non-atrous, atrous and modified, atrous with accuracy improvements like using Laplacian of Gaussian filters. Parameters such as LV end-diastolic diameter (LVEDD) and ejection fraction (LVEF) were quantified, and myocardial strains analyzed with the Radial Point Interpolation Method (RPIM). Myocardial classification was validated with the performance metrics of accuracy, Dice, average perpendicular distance (APD) and others. Repeated measures ANOVA and intraclass correlation (ICC) with Cronbach’s α (C-Alpha) tests were conducted between the three DCNNs and a vendor tool on chamber quantification and myocardial strain analysis. Results: Validation results in the same test-set for myocardial classification were accuracy = 97%, Dice = 0.92, APD = 1.2 mm with the modified ResNet-50, and accuracy = 95%, Dice = 0.90, APD = 1.7 mm with the atrous ResNet-50. The ICC results between the modified ResNet-50, atrous ResNet-50 and vendor-tool were C-Alpha = 0.97 for LVEF (55±7%, 54±7%, 54±7%, p = 0.6), and C-Alpha = 0.87 for LVEDD (4.6 ± 0.3 cm, 4.6 ± 0.3 cm, 4.6 ± 0.4 cm, p = 0.7). Conclusion: Similar performance metrics and equivalent parameters obtained from comparisons between the atrous networks and vendor tool show that segmentation with the modified, atrous DCNN is applicable for automated LV chamber quantification and subsequent strain analysis in cardiotoxicity. Advances in knowledge: A novel deep-learning technique for segmenting DENSE images was developed and validated for LV chamber quantification and strain analysis in cardiotoxicity detection.

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