TY - GEN
T1 - Future Unruptured Intracranial Aneurysm Growth Prediction Using Mesh Convolutional Neural Networks
AU - Timmins, Kimberley M.
AU - Kamphuis, Maarten J.
AU - Vos, Iris N.
AU - Velthuis, Birgitta K.
AU - Schaaf, Irene C.van der
AU - Kuijf, Hugo J.
N1 - Funding Information:
Acknowledgements. We acknowledge the support from the Netherlands Cardiovascular Research Initiative: An initiative with support of the Dutch Heart Foundation, CVON2015-08 ERASE and CVON2018-02 ANEURYSM@RISK.
Publisher Copyright:
© 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.
PY - 2022/12/17
Y1 - 2022/12/17
N2 - The growth of unruptured intracranial aneurysms (UIAs) is a predictor of rupture. Therefore, for further imaging surveillance and treatment planning, it is important to be able to predict if an UIA is likely to grow based on an initial baseline Time-of-Flight MRA (TOF-MRA). It is known that the size and shape of UIAs are predictors of aneurysm growth and/or rupture. We perform a feasibility study of using a mesh convolutional neural network for future UIA growth prediction from baseline TOF-MRAs. We include 151 TOF-MRAs, with 169 UIAs where 49 UIAs were classified as growing and 120 as stable, based on the clinical definition of growth (>1 mm increase in size in follow-up scan). UIAs were segmented from TOF-MRAs and meshes were automatically generated. We investigate the input of both UIA mesh only and region-of-interest (ROI) meshes including UIA and surrounding parent vessels. We develop a classification model to predict UIAs that will grow or remain stable. The model consisted of a mesh convolutional neural network including additional novel input edge features of shape index and curvedness which describe the surface topology. It was investigated if input edge mid-point co-ordinates influenced the model performance. The model with highest AUC (63.8%) for growth prediction was using UIA meshes with input edge mid-point co-ordinate features (average F1 score = 62.3%, accuracy = 66.9%, sensitivity = 57.3%, specificity = 70.8%). We present a future UIA growth prediction model based on a mesh convolutional neural network with promising results.
AB - The growth of unruptured intracranial aneurysms (UIAs) is a predictor of rupture. Therefore, for further imaging surveillance and treatment planning, it is important to be able to predict if an UIA is likely to grow based on an initial baseline Time-of-Flight MRA (TOF-MRA). It is known that the size and shape of UIAs are predictors of aneurysm growth and/or rupture. We perform a feasibility study of using a mesh convolutional neural network for future UIA growth prediction from baseline TOF-MRAs. We include 151 TOF-MRAs, with 169 UIAs where 49 UIAs were classified as growing and 120 as stable, based on the clinical definition of growth (>1 mm increase in size in follow-up scan). UIAs were segmented from TOF-MRAs and meshes were automatically generated. We investigate the input of both UIA mesh only and region-of-interest (ROI) meshes including UIA and surrounding parent vessels. We develop a classification model to predict UIAs that will grow or remain stable. The model consisted of a mesh convolutional neural network including additional novel input edge features of shape index and curvedness which describe the surface topology. It was investigated if input edge mid-point co-ordinates influenced the model performance. The model with highest AUC (63.8%) for growth prediction was using UIA meshes with input edge mid-point co-ordinate features (average F1 score = 62.3%, accuracy = 66.9%, sensitivity = 57.3%, specificity = 70.8%). We present a future UIA growth prediction model based on a mesh convolutional neural network with promising results.
KW - Aneurysms
KW - Geometric deep learning
KW - Growth prediction
KW - Meshes
KW - Topology
UR - http://www.scopus.com/inward/record.url?scp=85144825660&partnerID=8YFLogxK
U2 - 10.1007/978-3-031-23223-7_9
DO - 10.1007/978-3-031-23223-7_9
M3 - Conference contribution
AN - SCOPUS:85144825660
SN - 978-3-031-23222-0
T3 - Lecture Notes in Computer Science
SP - 103
EP - 112
BT - Ethical and Philosophical Issues in Medical Imaging, Multimodal Learning and Fusion Across Scales for Clinical Decision Support, and Topological Data Analysis for Biomedical Imaging
A2 - Baxter, John S.H.
A2 - Rekik, Islem
A2 - Eagleson, Roy
A2 - Zhou, Luping
A2 - Syeda-Mahmood, Tanveer
A2 - Wang, Hongzhi
A2 - Hajij, Mustafa
PB - Springer
CY - Cham
T2 - 1st International Workshop on Ethical and Philosophical Issues in Medical Imaging, EPIMI 2022, the 12th International Workshop on Multimodal Learning and Fusion Across Scales for Clinical Decision Support, ML-CDS 2022, and the 2nd International Workshop on Topological Data Analysis for Biomedical Imaging, TDA4BiomedicalImaging 2022, held in conjunction with the 25th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2022
Y2 - 18 September 2022 through 22 September 2022
ER -