A new deep neural segmentation network for cerebral aneurysms in 2D digital subtraction angiography

Satyananda Kashyap; Hakan Bulu; Ashutosh Jadhav; Ronak Dholakia; Amon Y. Liu; Hussain Rangwala; William R. Patterson; Mehdi Moradi

doi:10.1117/12.2611205

4 April 2022 A new deep neural segmentation network for cerebral aneurysms in 2D digital subtraction angiography

Satyananda Kashyap, Hakan Bulu, Ashutosh Jadhav, Ronak Dholakia, Amon Y. Liu, Hussain Rangwala, William R. Patterson, Mehdi Moradi

Author Affiliations +

Proceedings Volume 12034, Medical Imaging 2022: Image-Guided Procedures, Robotic Interventions, and Modeling; 1203404 (2022) https://doi.org/10.1117/12.2611205
Event: SPIE Medical Imaging, 2022, San Diego, California, United States

Abstract

Digital subtraction angiography (DSA) is routinely used for measuring the dimensions and characteristics of cerebral aneurysms as a step in planning of interventional treatments. Incorrect sizing of the aneurysm sac puts the patient at the risk of incomplete treatment due to the use of an intrasaccular implant that is too small or too large. In this work, we propose an automatic method to segment the aneurysm sac in 2D DSA images to enable fast and accurate measurements. We use a UNet-like architecture. However, we replace the encoder arm of this network with an EfficientNet architecture, pre-trained on 300 million natural images. We show that this architecture delivers very accurate segmentation of the aneurysm sac on a dataset of 144 DSA images obtained from patients prior to implantation of an intrasaccular device to treat wide-neck bifurcation aneurysms. We report a Dice coefficient of 0.9.

Conference Presentation

Citation Download Citation

Satyananda Kashyap, Hakan Bulu, Ashutosh Jadhav, Ronak Dholakia, Amon Y. Liu, Hussain Rangwala, William R. Patterson, and Mehdi Moradi "A new deep neural segmentation network for cerebral aneurysms in 2D digital subtraction angiography", Proc. SPIE 12034, Medical Imaging 2022: Image-Guided Procedures, Robotic Interventions, and Modeling, 1203404 (4 April 2022); https://doi.org/10.1117/12.2611205

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