DeepVox and SAVE-CT: a contrast- and dose-independent 3D deep learning approach for thoracic aorta segmentation and aneurysm prediction using computed tomography scans

• URL: http://arxiv.org/abs/2310.15328v1
• Date: Mon, 23 Oct 2023 19:48:58 GMT
• Title: DeepVox and SAVE-CT: a contrast- and dose-independent 3D deep learning approach for thoracic aorta segmentation and aneurysm prediction using computed tomography scans
• Authors: Matheus del-Valle, Lariza Laura de Oliveira, Henrique Cursino Vieira, Henrique Min Ho Lee, Lucas Lembran\c{c}a Pinheiro, Maria Fernanda Portugal, Newton Shydeo Brand\~ao Miyoshi, Nelson Wolosker
• Abstract summary: Thoracic aortic aneurysm (TAA) is a fatal disease which potentially leads to dissection or rupture through progressive enlargement of the aorta. Scans for other indications could help on this screening, however if acquired without contrast enhancement or with low dose protocol, it can make the clinical evaluation difficult. In this study, it was selected 587 unique CT scans including control and TAA patients, acquired with low and standard dose protocols, with or without contrast enhancement. A novel segmentation model, DeepVox, exhibited dice score coefficients of 0.932 and 0.897 for development and test sets, respectively, with faster training speed in comparison to models
• Score: 2.3135717943756307
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Thoracic aortic aneurysm (TAA) is a fatal disease which potentially leads to dissection or rupture through progressive enlargement of the aorta. It is usually asymptomatic and screening recommendation are limited. The gold-standard evaluation is performed by computed tomography angiography (CTA) and radiologists time-consuming assessment. Scans for other indications could help on this screening, however if acquired without contrast enhancement or with low dose protocol, it can make the clinical evaluation difficult, besides increasing the scans quantity for the radiologists. In this study, it was selected 587 unique CT scans including control and TAA patients, acquired with low and standard dose protocols, with or without contrast enhancement. A novel segmentation model, DeepVox, exhibited dice score coefficients of 0.932 and 0.897 for development and test sets, respectively, with faster training speed in comparison to models reported in the literature. The novel TAA classification model, SAVE-CT, presented accuracies of 0.930 and 0.922 for development and test sets, respectively, using only the binary segmentation mask from DeepVox as input, without hand-engineered features. These two models together are a potential approach for TAA screening, as they can handle variable number of slices as input, handling thoracic and thoracoabdominal sequences, in a fully automated contrast- and dose-independent evaluation. This may assist to decrease TAA mortality and prioritize the evaluation queue of patients for radiologists.

Related papers

• Automated Plaque Detection and Agatston Score Estimation on Non-Contrast CT Scans: A Multicenter Study [2.4476474544077225]
  The purpose of this study is to validate an automated cardiac plaque detection model using a 3D multiclass nnU-Net. In this work we demonstrate how the nnU-Net segmentation pipeline may be adapted to detect plaques in the coronary arteries and valves. With a linear correction, nnU-Net deep learning methods may also accurately estimate Agatston scores on chest non-contrast CT scans.
  arXiv  Detail & Related papers  (2024-02-14T20:41:37Z)
• 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)
• Automatic Classification of Symmetry of Hemithoraces in Canine and Feline Radiographs [0.0]
  We propose a hemithoraces segmentation method based on Convolutional Neural Networks (CNNs) and active contours. To test the robustness of the proposed thorax segmentation method to underexposure and overexposure, we synthetically corrupted properly exposed radiographs.
  arXiv  Detail & Related papers  (2023-02-24T22:46:16Z)
• Learning to diagnose cirrhosis from radiological and histological labels with joint self and weakly-supervised pretraining strategies [62.840338941861134]
  We propose to leverage transfer learning from large datasets annotated by radiologists, to predict the histological score available on a small annex dataset. We compare different pretraining methods, namely weakly-supervised and self-supervised ones, to improve the prediction of the cirrhosis. This method outperforms the baseline classification of the METAVIR score, reaching an AUC of 0.84 and a balanced accuracy of 0.75.
  arXiv  Detail & Related papers  (2023-02-16T17:06:23Z)
• Building Brains: Subvolume Recombination for Data Augmentation in Large Vessel Occlusion Detection [56.67577446132946]
  A large training data set is required for a standard deep learning-based model to learn this strategy from data. We propose an augmentation method that generates artificial training samples by recombining vessel tree segmentations of the hemispheres from different patients. In line with the augmentation scheme, we use a 3D-DenseNet fed with task-specific input, fostering a side-by-side comparison between the hemispheres.
  arXiv  Detail & Related papers  (2022-05-05T10:31:57Z)
• A Deep Learning Approach to Predicting Collateral Flow in Stroke Patients Using Radiomic Features from Perfusion Images [58.17507437526425]
  Collateral circulation results from specialized anastomotic channels which provide oxygenated blood to regions with compromised blood flow. The actual grading is mostly done through manual inspection of the acquired images. We present a deep learning approach to predicting collateral flow grading in stroke patients based on radiomic features extracted from MR perfusion data.
  arXiv  Detail & Related papers  (2021-10-24T18:58:40Z)
• Automated Detection of Coronary Artery Stenosis in X-ray Angiography using Deep Neural Networks [0.0]
  We propose a two-step deep-learning framework to partially automate the detection of stenosis from X-ray coronary angiography images. We achieved a 0.97 accuracy on the task of classifying the Left/Right Coronary Artery angle view and 0.68/0.73 recall on the determination of the regions of interest, for LCA and RCA, respectively.
  arXiv  Detail & Related papers  (2021-03-04T11:45:54Z)
• A new approach to extracting coronary arteries and detecting stenosis in invasive coronary angiograms [9.733630514873376]
  We aim to develop an automatic algorithm by deep learning to extract coronary arteries from ICAs. In this study, a multi-input and multi-scale (MIMS) U-Net with a two-stage recurrent training strategy was proposed for the automatic vessel segmentation. Experimental results demonstrated that the proposed method achieved an average Dice score of 0.8329, an average sensitivity of 0.8281, and an average specificity of 0.9979 in our dataset with 294 ICAs obtained from 73 patient.
  arXiv  Detail & Related papers  (2021-01-25T01:48:27Z)
• iPhantom: a framework for automated creation of individualized computational phantoms and its application to CT organ dosimetry [58.943644554192936]
  This study aims to develop and validate a novel framework, iPhantom, for automated creation of patient-specific phantoms or digital-twins. The framework is applied to assess radiation dose to radiosensitive organs in CT imaging of individual patients. iPhantom precisely predicted all organ locations with good accuracy of Dice Similarity Coefficients (DSC) >0.6 for anchor organs and DSC of 0.3-0.9 for all other organs.
  arXiv  Detail & Related papers  (2020-08-20T01:50:49Z)
• 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.