Comparing methods of detecting and segmenting unruptured intracranial aneurysms on TOF-MRAS: The ADAM challenge

Timmins, Kimberley M.; Schaaf, Irene C. van der; Bennink, Edwin; Ruigrok, Ynte M.; An, Xiangdong; Baumgärtner, Michael; Bourdon, Pascal; Feo, Riccardo De; Noto, Tommaso Di; Dubost, Florian; Fava-Sanches, Augusto; Feng, Xue; Giroud, Corentin; Hu, Minghui; Jaeger, Paul F.; Kaiponen, Juhana; Klimont, Michał; Li, Yuexiang; Li, Hongwei; Lin, Yi; Loehr, Timo; Ma, Jun; Maier‐Hein, Klaus H.; Marie, Guillaume; Menze, Bjoern; Richiardi, Jonas; Rjiba, Saifeddine; Shah, Dhaval; Shit, Suprosanna; Tohka, Jussi; Urruty, Thierry; Walińska, Urszula; Yang, Xiaoping; Yang, Yunqiao; Yin, Yin; Velthuis, Birgitta K.; Kuijf, Hugo J.

doi:10.1016/j.neuroimage.2021.118216

Cited by 42 publications

(39 citation statements)

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“…An alternative and reproducible automatic aneurysm segmentation method could be used. 23 There was variation in the time period between baseline and follow-up because the most recent follow-up MRA was always performed to ensure the longest follow-up time and potential largest proportion of growth and morphologic change. In some cases, the aneurysm was treated or ruptured after the first standard follow-up at 1 year, meaning that the time until follow-up was relatively short.…”

Section: Discussionmentioning

confidence: 99%

Relationship between 3D Morphologic Change and 2D and 3D Growth of Unruptured Intracranial Aneurysms

Timmins

Kuijf

Vergouwen

et al. 2022

AJNR Am J Neuroradiol

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BACKGROUND AND PURPOSE: Untreated unruptured intracranial aneurysms are usually followed radiologically to detect aneurysm growth, which is associated with increased rupture risk. The ideal aneurysm size cutoff for defining growth remains unclear and also whether change in morphology should be part of the definition. We investigated the relationship between change in aneurysm size and 3D quantified morphologic changes during follow-up. MATERIALS AND METHODS:We performed 3D morphology measurements of unruptured intracranial aneurysms on baseline and follow-up TOF-MRAs. Morphology measurements included surface area, compactness, elongation, flatness, sphericity, shape index, and curvedness. We investigated the relation between morphologic change between baseline and follow-up scans and unruptured intracranial aneurysm growth, with 2D and 3D growth defined as a continuous variable (correlation statistics) and a categoric variable (t test statistics). Categoric growth was defined as $1-mm increase in 2D length or width. We assessed unruptured intracranial aneurysms that changed in morphology and the proportion of growing and nongrowing unruptured intracranial aneurysms with statistically significant morphologic change.

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Section: Discussionmentioning

confidence: 99%

Relationship between 3D Morphologic Change and 2D and 3D Growth of Unruptured Intracranial Aneurysms

Timmins

Kuijf

Vergouwen

et al. 2022

AJNR Am J Neuroradiol

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“…In this study, we used the TOF-MRA source images and structural MRIs (T1, T2, FLAIR) from the Aneurysm Detection And segMentation Challenge (ADAM) dataset [27]. The dataset includes 113 diverse patient cases, each containing a TOF-MRA image, a structural magnetic resonance image scan (either T1, T2, or FLAIR), and an aligned image, as seen in Figure 1.…”

Section: Datasetmentioning

confidence: 99%

Computer-Assisted Detection Of Intracranial Aneurysms Using A Transformer Deep Neural Network In 3D MR Angiography

Zhou¹,

Huang

2022

Preprint

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Intracranial aneurysms, a brain condition caused by weakened blood vessels, result in abnormal bulging or dilation of the intracranial artery walls. Up to 5% of the global population will suffer from this disease, and early detection is critical. Due to their small size, aneurysms are often missed in initial patient image assessments. Further, the process of interpreting 3D medical images can be time-consuming, error-prone, and require trained radiologists. This study implemented a deep learning model using a Transformer Neural Network to enable the automated computer-assisted detection of aneurysms in 3D TOF-MRA images. This novel Transformer model demonstrated overall 91.9% patient-level sensitivity and 95.1% patient specificity in detecting intracranial aneurysms. Additionally, for aneurysms exceeding 5 mm in diameter, it reached patient sensitivity of 95.8% and patient specificity of 96.4%. To our knowledge, this is the first study to implement a Transformer Deep Neural Network for the detection of aneurysms from 3D volumetric TOF-MRA images. The successful results underscore the potential of Transformer models in accelerating diagnoses of cerebrovascular diseases while streamlining radiologists’ workflow. This detection method can be applicable for identifying aneurysms in other organs and diseases such as brain tumors.

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“…The data provided for these challenges were always limited to image data and corresponding vessel geometries of few aneurysms. The ADAM challenge at MICCAI 2020 enabled the training and comparison of detection and segmentation methods using machine learning algorithms ( Timmins et al, 2021 ). Annotations of MRA datasets were provided for unruptured intracranial aneurysms.…”

Section: Introductionmentioning

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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Comparing methods of detecting and segmenting unruptured intracranial aneurysms on TOF-MRAS: The ADAM challenge

Cited by 42 publications

References 50 publications

Relationship between 3D Morphologic Change and 2D and 3D Growth of Unruptured Intracranial Aneurysms

Relationship between 3D Morphologic Change and 2D and 3D Growth of Unruptured Intracranial Aneurysms

Computer-Assisted Detection Of Intracranial Aneurysms Using A Transformer Deep Neural Network In 3D MR Angiography

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

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