Abdominal Aortic Aneurysm Segmentation Using Convolutional Neural Networks Trained with Images Generated with a Synthetic Shape Model

López-Linares, Karen; Stephens, Maialen; Garcı́a, Inmaculada; Macía, Iván; Ballester, Miguel Ángel González; Estépar, Raúl San Jośe

doi:10.1007/978-3-030-33327-0_20

Cited by 4 publications

(2 citation statements)

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“…The use of synthetic data generated by the expert system as an input for the DL process creates a continuous improvement loop for the hybrid approach. Other investigators recently proposed a similar approach by training a CNN with images generated with a synthetic shape model in order to segment AAA from CTA-scans dataset [ 42 ]. The results showed that the performance of a CNN trained with synthetic data to segment AAAs from new scans was comparable to the one of a network trained with real images.…”

Section: Discussionmentioning

confidence: 99%

Automated Segmentation of the Human Abdominal Vascular System Using a Hybrid Approach Combining Expert System and Supervised Deep Learning

Lareyre

Adam

Carrier

et al. 2021

JCM

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Background: Computed tomography angiography (CTA) is one of the most commonly used imaging technique for the management of vascular diseases. Here, we aimed to develop a hybrid method combining a feature-based expert system with a supervised deep learning (DL) algorithm to enable a fully automatic segmentation of the abdominal vascular tree. Methods: We proposed an algorithm based on the hybridization of a data-driven convolutional neural network and a knowledge-based model dedicated to vascular system segmentation. By using two distinct datasets of CTA from patients to evaluate independence to training dataset, the accuracy of the hybrid method for lumen and thrombus segmentation was evaluated compared to the feature-based expert system alone and to the ground truth provided by a human expert. Results: The hybrid approach demonstrated a better accuracy for lumen segmentation compared to the expert system alone (volume similarity: 0.8128 vs. 0.7912, p = 0.0006 and Dice similarity coefficient: 0.8266 vs. 0.7942, p < 0.0001). The accuracy for thrombus segmentation was also enhanced using the hybrid approach (volume similarity: 0.9404 vs. 0.9185, p = 0.0027 and Dice similarity coefficient: 0.8918 vs. 0.8654, p < 0.0001). Conclusions: By enabling a robust and fully automatic segmentation, the method could be used to develop real-time decision support to help in the management of vascular diseases.

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Section: Discussionmentioning

confidence: 99%

Automated Segmentation of the Human Abdominal Vascular System Using a Hybrid Approach Combining Expert System and Supervised Deep Learning

Lareyre

Adam

Carrier

et al. 2021

JCM

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“…Deep learning neural networks have also been successfully applied and tested for image segmentation from optical coherence tomography, such as to identify the aortic intimal layer in the rat [14]. Convolutional neural networks (CNN), a type of neural networks that do not change even when certain input data undergo numerical transformations [2], have been also utilized for segmentation of images, focused on applications in predicting AAA outcomes [15]. The ability to generalize the use of a CNN trained using synthetic images was tested on a set of real medical images, and the CNN was found to provide high detection accuracy [15].…”

Section: Image Analysis and Segmentationmentioning

confidence: 99%

Artificial Intelligence for Developing Tools and Technologies in Vascular and Cardiac Surgery Applications: A Survey

Alamaniotis¹

2021

AJBSR

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Deep learning – promises for 3D nuclear imaging: a guide for biologists

Mougeot

Dubos

Chausse

et al. 2022

Journal of Cell Science

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For the past century, the nucleus has been the focus of extensive investigations in cell biology. However, many questions remain about how its shape and size are regulated during development, in different tissues, or during disease and aging. To track these changes, microscopy has long been the tool of choice. Image analysis has revolutionized this field of research by providing computational tools that can be used to translate qualitative images into quantitative parameters. Many tools have been designed to delimit objects in 2D and, eventually, in 3D in order to define their shapes, their number or their position in nuclear space. Today, the field is driven by deep-learning methods, most of which take advantage of convolutional neural networks. These techniques are remarkably adapted to biomedical images when trained using large datasets and powerful computer graphics cards. To promote these innovative and promising methods to cell biologists, this Review summarizes the main concepts and terminologies of deep learning. Special emphasis is placed on the availability of these methods. We highlight why the quality and characteristics of training image datasets are important and where to find them, as well as how to create, store and share image datasets. Finally, we describe deep-learning methods well-suited for 3D analysis of nuclei and classify them according to their level of usability for biologists. Out of more than 150 published methods, we identify fewer than 12 that biologists can use, and we explain why this is the case. Based on this experience, we propose best practices to share deep-learning methods with biologists.

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Abdominal Aortic Aneurysm Segmentation Using Convolutional Neural Networks Trained with Images Generated with a Synthetic Shape Model

Cited by 4 publications

References 10 publications

Automated Segmentation of the Human Abdominal Vascular System Using a Hybrid Approach Combining Expert System and Supervised Deep Learning

Automated Segmentation of the Human Abdominal Vascular System Using a Hybrid Approach Combining Expert System and Supervised Deep Learning

Artificial Intelligence for Developing Tools and Technologies in Vascular and Cardiac Surgery Applications: A Survey

Deep learning – promises for 3D nuclear imaging: a guide for biologists

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Abdominal Aortic Aneurysm Segmentation Using Convolutional Neural Networks Trained with Images Generated with a Synthetic Shape Model

Cited by 4 publications

References 10 publications

Automated Segmentation of the Human Abdominal Vascular System Using a Hybrid Approach Combining Expert System and Supervised Deep Learning

Automated Segmentation of the Human Abdominal Vascular System Using a Hybrid Approach Combining Expert System and Supervised Deep Learning

Artificial Intelligence for Developing Tools and Technologies in Vascular and Cardiac Surgery Applications: A Survey

Deep learning ­– promises for 3D nuclear imaging: a guide for biologists

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Deep learning – promises for 3D nuclear imaging: a guide for biologists