A new method incorporating deep learning with shape priors for left ventricular segmentation in myocardial perfusion SPECT images

• URL: http://arxiv.org/abs/2206.03603v1
• Date: Tue, 7 Jun 2022 22:12:11 GMT
• Title: A new method incorporating deep learning with shape priors for left ventricular segmentation in myocardial perfusion SPECT images
• Authors: Fubao Zhu, Jinyu Zhao, Chen Zhao, Shaojie Tang, Jiaofen Nan, Yanting Li, Zhongqiang Zhao, Jianzhou Shi, Zenghong Chen, Zhixin Jiang, Weihua Zhou
• Abstract summary: The assessment of left ventricular (LV) function by myocardial perfusion SPECT (MPS) relies on accurate myocardial segmentation. The purpose of this paper is to develop and validate a new method incorporating deep learning with shape priors to accurately extract the LV myocardium for automatic measurement of LV functional parameters.
• Score: 14.185169567232055
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Background: The assessment of left ventricular (LV) function by myocardial perfusion SPECT (MPS) relies on accurate myocardial segmentation. The purpose of this paper is to develop and validate a new method incorporating deep learning with shape priors to accurately extract the LV myocardium for automatic measurement of LV functional parameters. Methods: A segmentation architecture that integrates a three-dimensional (3D) V-Net with a shape deformation module was developed. Using the shape priors generated by a dynamic programming (DP) algorithm, the model output was then constrained and guided during the model training for quick convergence and improved performance. A stratified 5-fold cross-validation was used to train and validate our models. Results: Results of our proposed method agree well with those from the ground truth. Our proposed model achieved a Dice similarity coefficient (DSC) of 0.9573(0.0244), 0.9821(0.0137), and 0.9903(0.0041), a Hausdorff distances (HD) of 6.7529(2.7334) mm, 7.2507(3.1952) mm, and 7.6121(3.0134) mm in extracting the endocardium, myocardium, and epicardium, respectively. Conclusion: Our proposed method achieved a high accuracy in extracting LV myocardial contours and assessing LV function.

Related papers

• An Improved Approach for Cardiac MRI Segmentation based on 3D UNet Combined with Papillary Muscle Exclusion [0.0]
  It is crucial to develop robust algorithms for the precise segmentation of the heart structure during different phases. In this work, an improved 3D UNet model is introduced to segment the myocardium and LV, while excluding papillary muscles. For the practical testing of the proposed framework, a total of 8,400 cardiac MRI images were collected and analysed.
  arXiv  Detail & Related papers  (2024-10-09T12:19:58Z)
• KLDD: Kalman Filter based Linear Deformable Diffusion Model in Retinal Image Segmentation [51.03868117057726]
  This paper proposes a novel Kalman filter based Linear Deformable Diffusion (KLDD) model for retinal vessel segmentation. Our model employs a diffusion process that iteratively refines the segmentation, leveraging the flexible receptive fields of deformable convolutions. Experiments are evaluated on retinal fundus image datasets (DRIVE, CHASE_DB1) and the 3mm and 6mm of the OCTA-500 dataset.
  arXiv  Detail & Related papers  (2024-09-19T14:21:38Z)
• Semantic-aware Temporal Channel-wise Attention for Cardiac Function Assessment [69.02116920364311]
  Existing video-based methods do not pay much attention to the left ventricular region, nor the left ventricular changes caused by motion. We propose a semi-supervised auxiliary learning paradigm with a left ventricular segmentation task, which contributes to the representation learning for the left ventricular region. Our approach achieves state-of-the-art performance on the Stanford dataset with an improvement of 0.22 MAE, 0.26 RMSE, and 1.9% $R2$.
  arXiv  Detail & Related papers  (2023-10-09T05:57:01Z)
• Bayesian Optimization of 2D Echocardiography Segmentation [2.6947715121689204]
  We use BO to optimize the architectural and training-related hyper parameters of a deep convolutional neural network model. Dice overlaps of 0.95, 0.96, and 0.93 on left ventricular (LV) endocardium, LV epicardium, and left atrium respectively. We also observe significant improvement in derived clinical indices, including smaller median absolute errors for LV end-diastolic volume.
  arXiv  Detail & Related papers  (2022-11-17T20:52:36Z)
• Three-dimensional micro-structurally informed in silico myocardium -- towards virtual imaging trials in cardiac diffusion weighted MRI [58.484353709077034]
  We propose a novel method to generate a realistic numerical phantom of myocardial microstructure. In-silico tissue models enable evaluating quantitative models of magnetic resonance imaging.
  arXiv  Detail & Related papers  (2022-08-22T22:01:44Z)
• Steerable Pyramid Transform Enables Robust Left Ventricle Quantification [24.186574235693826]
  We describe a simple yet effective method to learn robust models for left ventricle (LV) quantification. Our success hinges on employing the biologically inspired steerable pyramid transform (SPT) for fixed front-end processing. Our SPT-augmented model not only achieves reasonable prediction accuracy compared to state-of-the-art methods, but also exhibits significantly improved robustness against input perturbations.
  arXiv  Detail & Related papers  (2022-01-20T06:09:56Z)
• Systematic Clinical Evaluation of A Deep Learning Method for Medical Image Segmentation: Radiosurgery Application [48.89674088331313]
  We systematically evaluate a Deep Learning (DL) method in a 3D medical image segmentation task. Our method is integrated into the radiosurgery treatment process and directly impacts the clinical workflow.
  arXiv  Detail & Related papers  (2021-08-21T16:15:40Z)
• Spatio-temporal Multi-task Learning for Cardiac MRI Left Ventricle Quantification [6.887389908965403]
  We propose a learning-temporal multi-task approach to obtain a complete set of measurements of cardiac left ventricle (LV) morphology. We first segment LVs using an encoder-decoder network and then introduce a framework to regress 11 LV indices and classify the cardiac phase. The proposed model is based on the 3D-temporal convolutions, which extract spatial and features from MR images.
  arXiv  Detail & Related papers  (2020-12-24T17:48:35Z)
• Revisiting 3D Context Modeling with Supervised Pre-training for Universal Lesion Detection in CT Slices [48.85784310158493]
  We propose a Modified Pseudo-3D Feature Pyramid Network (MP3D FPN) to efficiently extract 3D context enhanced 2D features for universal lesion detection in CT slices. With the novel pre-training method, the proposed MP3D FPN achieves state-of-the-art detection performance on the DeepLesion dataset. The proposed 3D pre-trained weights can potentially be used to boost the performance of other 3D medical image analysis tasks.
  arXiv  Detail & Related papers  (2020-12-16T07:11:16Z)
• Segmentation of the Myocardium on Late-Gadolinium Enhanced MRI based on 2.5 D Residual Squeeze and Excitation Deep Learning Model [55.09533240649176]
  The aim of this work is to develop an accurate automatic segmentation method based on deep learning models for the myocardial borders on LGE-MRI. A total number of 320 exams (with a mean number of 6 slices per exam) were used for training and 28 exams used for testing. The performance analysis of the proposed ensemble model in the basal and middle slices was similar as compared to intra-observer study and slightly lower at apical slices.
  arXiv  Detail & Related papers  (2020-05-27T20:44:38Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.