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.
With the advent of computers and natural language processing, it is not surprising to see that humans are trying to use computers to answer questions. By the 1960s, there were systems implemented on the two major models of question answering, IR-based and knowledge-based, to answer questions about sport statistics and scientific facts. This paper reports on the development of a knowledge-based question answering system that is aimed at providing cognitive assistance to radiologists. Our system represents the question as a semantic query to a medical knowledge base. Evidence obtained from textual and imaging data associated with the question is then combined to arrive at an answer. This question answering system has 3 stages: i) question text and answer choices processing, ii) image processing, and iii) reasoning. Currently, the system can answer differential diagnosis and patient management questions, however, we can tackle a wider variety of question types by improving our medical knowledge coverage in the future.
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