AI4Code
Srikanth Tamilselvam, Dinesh Khandelwal, et al.
ACML 2022
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.
Srikanth Tamilselvam, Dinesh Khandelwal, et al.
ACML 2022
Eduardo Almeida Soares, Victor Shirasuna, et al.
ACS Fall 2024
Andrew Rouditchenko, Angie Boggust, et al.
INTERSPEECH 2021
Conrad Albrecht, Jannik Schneider, et al.
CVPR 2025