Evaluation of state-of-the-art deep learning models in the segmentation of the heart ventricles in parasternal short-axis echocardiograms

• URL: http://arxiv.org/abs/2503.08970v1
• Date: Wed, 12 Mar 2025 00:33:01 GMT
• Title: Evaluation of state-of-the-art deep learning models in the segmentation of the heart ventricles in parasternal short-axis echocardiograms
• Authors: Julian Rene Cuellar Buritica, Vu Dinh, Manjula Burri, Julie Roelandts, James Wendling, Jon D. Klingensmith,
• Abstract summary: Deep-learning models were evaluated on the segmentation of the ventricles in parasternal short-axis echocardiograms (PSAX-echo)<n>Deep-learning models are suitable for the segmentation of the left and right ventricles in PSAX-echo.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Previous studies on echocardiogram segmentation are focused on the left ventricle in parasternal long-axis views. In this study, deep-learning models were evaluated on the segmentation of the ventricles in parasternal short-axis echocardiograms (PSAX-echo). Segmentation of the ventricles in complementary echocardiogram views will allow the computation of important metrics with the potential to aid in diagnosing cardio-pulmonary diseases and other cardiomyopathies. Evaluating state-of-the-art models with small datasets can reveal if they improve performance on limited data. PSAX-echo were performed on 33 volunteer women. An experienced cardiologist identified end-diastole and end-systole frames from 387 scans, and expert observers manually traced the contours of the cardiac structures. Traced frames were pre-processed and used to create labels to train 2 specific-domain (Unet-Resnet101 and Unet-ResNet50), and 4 general-domain (3 Segment Anything (SAM) variants, and the Detectron2) deep-learning models. The performance of the models was evaluated using the Dice similarity coefficient (DSC), Hausdorff distance (HD), and difference in cross-sectional area (DCSA). The Unet-Resnet101 model provided superior performance in the segmentation of the ventricles with 0.83, 4.93 pixels, and 106 pixel2 on average for DSC, HD, and DCSA respectively. A fine-tuned MedSAM model provided a performance of 0.82, 6.66 pixels, and 1252 pixel2, while the Detectron2 model provided 0.78, 2.12 pixels, and 116 pixel2 for the same metrics respectively. Deep-learning models are suitable for the segmentation of the left and right ventricles in PSAX-echo. This study demonstrated that specific-domain trained models such as Unet-ResNet provide higher accuracy for echo segmentation than general-domain segmentation models when working with small and locally acquired datasets.

Related papers

• Myocardial Region-guided Feature Aggregation Net for Automatic Coronary artery Segmentation and Stenosis Assessment using Coronary Computed Tomography Angiography [13.885760158090692]
  Myocardial Region-guided Feature Aggregation Net is a novel U-shaped dual-encoder architecture that integrates anatomical prior knowledge to enhance robustness in coronary artery segmentation. Our framework incorporates three key innovations: (1) a Myocardial Region-guided Module that directs attention to coronary regions via bridging expansion and multi-scale feature fusion, (2) a Residual Feature Extraction Module that combines parallel spatial channel attention with residual blocks to enhance local-global feature discrimination, and (3) a Multi-scale Feature Fusion Module for adaptive aggregation of hierarchical vascular features.
  arXiv  Detail & Related papers  (2025-04-27T16:43:52Z)
• Cardiac MRI Semantic Segmentation for Ventricles and Myocardium using Deep Learning [0.0]
  Automated noninvasive cardiac diagnosis plays a critical role in the early detection of cardiac disorders. We present a model to improve semantic segmentation of cardiac images.
  arXiv  Detail & Related papers  (2025-04-18T00:54:30Z)
• MSV-Mamba: A Multiscale Vision Mamba Network for Echocardiography Segmentation [8.090155401012169]
  Mamba, an emerging model, is one of the most cutting-edge approaches that is widely applied to diverse vision and language tasks.<n>This paper introduces a U-shaped deep learning model incorporating a large-window multiscale mamba module and a hierarchical feature fusion approach for echocardiographic segmentation.
  arXiv  Detail & Related papers  (2025-01-13T08:22:10Z)
• 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)
• TotalSegmentator MRI: Robust Sequence-independent Segmentation of Multiple Anatomic Structures in MRI [59.86827659781022]
  A nnU-Net model (TotalSegmentator) was trained on MRI and segment 80atomic structures.<n>Dice scores were calculated between the predicted segmentations and expert reference standard segmentations to evaluate model performance.<n>Open-source, easy-to-use model allows for automatic, robust segmentation of 80 structures.
  arXiv  Detail & Related papers  (2024-05-29T20:15:54Z)
• Accurate Fine-Grained Segmentation of Human Anatomy in Radiographs via Volumetric Pseudo-Labeling [66.75096111651062]
  We created a large-scale dataset of 10,021 thoracic CTs with 157 labels. We applied an ensemble of 3D anatomy segmentation models to extract anatomical pseudo-labels. Our resulting segmentation models demonstrated remarkable performance on CXR.
  arXiv  Detail & Related papers  (2023-06-06T18:01:08Z)
• 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)
• TotalSegmentator: robust segmentation of 104 anatomical structures in CT images [48.50994220135258]
  We present a deep learning segmentation model for body CT images. The model can segment 104 anatomical structures relevant for use cases such as organ volumetry, disease characterization, and surgical or radiotherapy planning.
  arXiv  Detail & Related papers  (2022-08-11T15:16:40Z)
• Improving Classification Model Performance on Chest X-Rays through Lung Segmentation [63.45024974079371]
  We propose a deep learning approach to enhance abnormal chest x-ray (CXR) identification performance through segmentations. Our approach is designed in a cascaded manner and incorporates two modules: a deep neural network with criss-cross attention modules (XLSor) for localizing lung region in CXR images and a CXR classification model with a backbone of a self-supervised momentum contrast (MoCo) model pre-trained on large-scale CXR data sets.
  arXiv  Detail & Related papers  (2022-02-22T15:24:06Z)
• Towards Fully Automated Segmentation of Rat Cardiac MRI by Leveraging Deep Learning Frameworks [1.6020567943077142]
  We develop segmentation models that expand on the standard U-Net architecture and evaluate separate models for systole and diastole phases. Applying Gaussian Processes to 1MSA allows to automate the selection of systole and diastole phases.
  arXiv  Detail & Related papers  (2021-09-09T11:48:50Z)
• 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)
• A Global Benchmark of Algorithms for Segmenting Late Gadolinium-Enhanced Cardiac Magnetic Resonance Imaging [90.29017019187282]
  " 2018 Left Atrium Challenge" using 154 3D LGE-MRIs, currently the world's largest cardiac LGE-MRI dataset. Analyse of the submitted algorithms using technical and biological metrics was performed. Results show the top method achieved a dice score of 93.2% and a mean surface to a surface distance of 0.7 mm.
  arXiv  Detail & Related papers  (2020-04-26T08:49:17Z)
• CondenseUNet: A Memory-Efficient Condensely-Connected Architecture for Bi-ventricular Blood Pool and Myocardium Segmentation [0.0]
  We propose a novel memory-efficient Convolutional Neural Network (CNN) architecture as a modification of both CondenseNet and DenseNet. Our experiments show that the proposed architecture runs on the Automated Cardiac Diagnosis Challenge dataset.
  arXiv  Detail & Related papers  (2020-04-05T16:34:51Z)

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.