A deep learning model for brain vessel segmentation in 3DRA with arteriovenous malformations

García, Camila; Fang, Yibin; Liu, Jianmin; Narata, Ana Paula; Orlando, José Ignacio; Larrabide, Ignacio

doi:10.1117/12.2669916

Cited by 6 publications

(3 citation statements)

References 28 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…27,28 Following Garcia et al's results, these modalities may be more thoroughly studied in AI models for AVM segmentation in the upcoming years. 29 Diverging a bit from morphological features, a study evidenced the robustness of quantitative DSA (QDSA) features of a selected ROI (e.g. peak density and time to peak) to predict the rupture of the AVM nidus.…”

Section: Hemorrhagic Cerebrovascular Diseasesmentioning

confidence: 99%

See 1 more Smart Citation

The new era of artificial intelligence in neuroradiology: current research and promising tools

Carvalho Macruz,

Dias,

Andrade

et al. 2024

Arq Neuropsiquiatr

View full text Add to dashboard Cite

Radiology has a number of characteristics that make it an especially suitable medical discipline for early artificial intelligence (AI) adoption. These include having a well-established digital workflow, standardized protocols for image storage, and numerous well-defined interpretive activities. The more than 200 commercial radiologic AI-based products recently approved by the Food and Drug Administration (FDA) to assist radiologists in a number of narrow image-analysis tasks such as image enhancement, workflow triage, and quantification, corroborate this observation. However, in order to leverage AI to boost efficacy and efficiency, and to overcome substantial obstacles to widespread successful clinical use of these products, radiologists should become familiarized with the emerging applications in their particular areas of expertise. In light of this, in this article we survey the existing literature on the application of AI-based techniques in neuroradiology, focusing on conditions such as vascular diseases, epilepsy, and demyelinating and neurodegenerative conditions. We also introduce some of the algorithms behind the applications, briefly discuss a few of the challenges of generalization in the use of AI models in neuroradiology, and skate over the most relevant commercially available solutions adopted in clinical practice. If well designed, AI algorithms have the potential to radically improve radiology, strengthening image analysis, enhancing the value of quantitative imaging techniques, and mitigating diagnostic errors.

show abstract

Section: Hemorrhagic Cerebrovascular Diseasesmentioning

confidence: 99%

“… 27 28 Following Garcia et al's results, these modalities may be more thoroughly studied in AI models for AVM segmentation in the upcoming years. 29 …”

Section: Ai In Cerebrovascular Diseasesmentioning

confidence: 99%

The new era of artificial intelligence in neuroradiology: current research and promising tools

Carvalho Macruz,

Dias,

Andrade

et al. 2024

Arq Neuropsiquiatr

View full text Add to dashboard Cite

show abstract

“…It should be noted that as Moccia et al 4 and Garcia et al 57 recently mentioned, the success of the segmentation approaches is highly influenced not only by the algorithm but also by factors such as imaging modalities the presence/absence of noise or artifacts, and the anatomical region of interest. This makes direct comparisons among the studies in the literature challenging.…”

Section: Convolutional Neural Network Based Models (Cnns)mentioning

confidence: 99%

Deep Learning for 3D Vascular Segmentation in Phase Contrast Tomography

Yagis,

Aslani,

Jain

et al. 2024

Preprint

View full text Add to dashboard Cite

Automated blood vessel segmentation is critical for biomedical image analysis, as vessel morphology changes are associated with numerous pathologies. Still, precise segmentation is difficult due to the complexity of vascular structures, anatomical variations across patients, the scarcity of annotated public datasets, and the quality of images. Our goal is to provide a foundation on the topic and identify a robust baseline model for application to vascular segmentation using a new imaging modality, Hierarchical Phase-Contrast Tomography (HiP-CT). We begin with an extensive review of current machine learning approaches for vascular segmentation across various organs. Our work introduces a meticulously curated training dataset, verified by double annotators, consisting of vascular data from three kidneys imaged using Hierarchical Phase-Contrast Tomography (HiP-CT) as part of the Human Organ Atlas Project. HiP-CT, pioneered at the European Synchrotron Radiation Facility in 2020, revolutionizes 3D organ imaging by offering resolution around 20µm/voxel, and enabling highly detailed localized zooms up to 1µm/voxel without physical sectioning. We leverage the nnU-Net framework to evaluate model performance on this high-resolution dataset, using both known and novel samples, and implementing metrics tailored for vascular structures. Our comprehensive review and empirical analysis on HiP-CT data sets a new standard for evaluating machine learning models in high-resolution organ imaging. Our three experiments yielded Dice scores of 0.9523 and 0.9410, and 0.8585, respectively. Nevertheless, DSC primarily assesses voxel-to-voxel concordance, overlooking several crucial characteristics of the vessels and should not be the sole metric for deciding the performance of vascular segmentation. Our results show that while segmentations yielded reasonably high scores—such as centerline Dice values ranging from 0.82 to 0.88, certain errors persisted. Specifically, large vessels that collapsed due to the lack of hydro-static pressure (HiP-CT is an ex vivo technique) were segmented poorly. Moreover, decreased connectivity in finer vessels and higher segmentation errors at vessel boundaries were observed. Such errors, particularly in significant vessels, obstruct the understanding of the structures by interrupting vascular tree connectivity. Through our review and outputs, we aim to set a benchmark for subsequent model evaluations using various modalities, especially with the HiP-CT imaging database.

show abstract

Comparative Study of Automated Algorithms for Brain Arteriovenous Malformation Nidus Extent Identification Using 3DRA

García,

Narata,

Liu

et al. 2023

Cardiovasc Eng Tech

View full text Add to dashboard Cite

A deep learning model for brain vessel segmentation in 3DRA with arteriovenous malformations

Cited by 6 publications

References 28 publications

The new era of artificial intelligence in neuroradiology: current research and promising tools

The new era of artificial intelligence in neuroradiology: current research and promising tools

Deep Learning for 3D Vascular Segmentation in Phase Contrast Tomography

Comparative Study of Automated Algorithms for Brain Arteriovenous Malformation Nidus Extent Identification Using 3DRA

Contact Info

Product

Resources

About