A Fully Automatic based Deep Learning Approach for Aneurysm Detection in DSA Images

Rahmany, Ines; Guetari, Ramzi; Khlifa, Nawrès

doi:10.1109/ipas.2018.8708897

Cited by 11 publications

(7 citation statements)

References 12 publications

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“…Because the detection of small aneurysms is essential in clinical routine, it is important to focus future efforts on a solution to this specific problem. Other publications verify these conclusions [20][21][22][23][24], showing a similar sensitivity and difficulty in the detection of small structures.…”

Section: Discussionsupporting

confidence: 57%

Cerebral Aneurysm Detection and Analysis Challenge 2020 (CADA)

Ivantsits

Goubergrits

Kuhnigk

et al. 2021

Lecture Notes in Computer Science

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Rupture of an intracranial aneurysm often results in subarachnoid hemorrhage, a life-threatening condition with high mortality and morbidity. The Cerebral Aneurysm Detection and Analysis (CADA) competition was organized to support the development and benchmarking of algorithms for the detection, analysis, and risk assessment of cerebral aneurysms in X-ray rotational angiography (3DRA) images. 109 anonymized 3DRA datasets were provided for training, and 22 additional datasets were used to test the algorithmic solutions. Cerebral aneurysm detection was assessed using the F2 score based on recall and precision, and the fit of the delivered bounding box was assessed using the distance to the aneurysm. Segmentation quality was measured using Jaccard and a combination of different surface distance measurements. Systematic errors were analyzed using volume correlation and bias. Rupture risk assessment was evaluated using the F2 score. 158 participants from 22 countries registered for the CADAchallenge. The detection solutions presented by the community are mostly accurate (F2 score 0.92) with a small number of missed aneurysms with diameters of 3.5 mm. In addition, the delineation of these structures is very good with a Jaccard score of 0.915. The rupture risk estimation methods achieved an F2 score of 0.7. The performance of the detection and segmentation solutions is equivalent to that of human experts. In rupture risk estimation, the best results are obtained by combining different image-based, morphological and computational fluid dynamic parameters using machine learning methods.

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Section: Discussionsupporting

confidence: 57%

Cerebral Aneurysm Detection and Analysis Challenge 2020 (CADA)

Ivantsits

Goubergrits

Kuhnigk

et al. 2021

Lecture Notes in Computer Science

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show abstract

“…Because detecting small aneurysms is essential in clinical routine, it is essential to focus future efforts on a solution to this specific problem. Other publications verify these conclusions ( Sulayman et al, 2016;Rahmany et al, 2018;Lauric et al, 2010;Jin et al, 2020;Zeng et al, 2020 ), showing a similar sensitivity and difficulty in the detection of small structures.…”

Section: Aneurysm Detectionsupporting

confidence: 57%

“…Nakao et al (2017) was the first to show a successful DL architecture based on a 2D network with nine different planes cutting the 3D volume, resulting in a sensitivity of 94%, and 2.8 false positives per patient. Rahmany et al (2018) suggested a non-end-to-end DL architecture, operating on a sliding window followed by a classification network. Ueda et al (2019) and Joo et al (2020) proposed different variations of the Res-Net model, utilizing 2D convolutions and 3D convolutions, respectively.…”

Section: Task 1: Aneurysm Detectionmentioning

confidence: 99%

Detection and analysis of cerebral aneurysms based on X-ray rotational angiography - the CADA 2020 challenge

Ivantsits

Goubergrits

Kuhnigk

et al. 2022

Medical Image Analysis

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“…For 2D raw data, Podgorsak, et al [ 62 ] directly used a CNN to detect and segment intracranial aneurysms in DSA, and the extracted angiographic parametric imaging radiomic features showed fewer features than human user results. Similar to 3D patch splitting, both Rahmany, et al [ 63 ] and Ueda, et al [ 64 ] extracted 2D patches (window) from raw images and trained on some well-known networks, including pretrained Inception-V3 [ 65 ] and untrained ResNet-18 [ 66 ] separately. However, these patch-based studies classified patients at the patch level and lacked specific statements about patient-level decisions.…”

Section: Resultsmentioning

confidence: 99%

A Review of Artificial Intelligence in Cerebrovascular Disease Imaging: Applications and Challenges

Chen

et al. 2022

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Background: A variety of emerging medical imaging technologies based on artificial intelligence have been widely applied in many diseases, but they are still limited used in the cerebrovascular field even though the diseases can lead to catastrophic consequences. Objective: This work aims to discuss the current challenges and future directions of artificial intelligence technology in cerebrovascular diseases through reviewing the existing literature related to applications in terms of computer-aided detection, prediction and treatment of cerebrovascular diseases. Methods: Based on artificial intelligence applications in four representative cerebrovascular diseases including intracranial aneurysm, arteriovenous malformation, arteriosclerosis and moyamoya disease, this paper systematically reviews studies published between 2006 and 2021 in five databases: National Center for Biotechnology Information, Elsevier Science Direct, IEEE Xplore Digital Library, Web of Science and Springer Link. And three refinement steps were further conducted after identifying relevant literature from these databases. Results: For the popular research topic, most of the included publications involved computer-aided detection and prediction of aneurysms, while studies about arteriovenous malformation, arteriosclerosis and moyamoya disease showed an upward trend in recent years. For the algorithms, both conventional machine learning and deep learning algorithms were utilized in these publications, but machine learning techniques accounted for a larger proportion. Conclusion: Algorithms related to artificial intelligence, especially deep learning, are promising tools for medical imaging analysis and will enhance the performance of computer-aided detection, prediction and treatment of cerebrovascular diseases.

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A Fully Automatic based Deep Learning Approach for Aneurysm Detection in DSA Images

Cited by 11 publications

References 12 publications

Cerebral Aneurysm Detection and Analysis Challenge 2020 (CADA)

Cerebral Aneurysm Detection and Analysis Challenge 2020 (CADA)

Detection and analysis of cerebral aneurysms based on X-ray rotational angiography - the CADA 2020 challenge

A Review of Artificial Intelligence in Cerebrovascular Disease Imaging: Applications and Challenges

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