Applying machine learning and image feature extraction techniques to the problem of cerebral aneurysm rupture

Abstract

Cerebral aneurysm is a cerebrovascular disorder characterized by a bulging in a weak area in the wall of an artery that supplies blood to the brain. It is relevant to understand the mechanisms leading to the apparition of aneurysms, their growth and, more important, leading to their rupture. The purpose of this study is to study the impact on aneurysm rupture of the combination of different parameters, instead of focusing on only one factor at a time as is frequently found in the literature, using machine learning and feature extraction techniques. This discussion takes relevance in the context of the complex decision that the physicians have to take to decide which therapy to apply, as each intervention bares its own risks, and implies to use a complex ensemble of resources (human resources, OR, etc.) in hospitals always under very high work load.This project has been raised in our actual working team, composed of interventional neuroradiologist, radiologic technologist, informatics engineers and biomedical engineers, "interdisciplinary platform for innovation in health", as part of a bigger project leaded by Universidad de Valparaiso (PMI UVA1402). It is relevant to emphasize that this project is made feasible by the existence of this network between physicians and engineers, and by the existence of data already registered in an orderly manner, structured and recorded in digital format.The present proposal arises from the description in nowadays literature that the actual indicators, whether based on morphological description of the aneurysm, or based on characterization of biomechanical factor or others, these indicators were shown not to provide sufficient information in order to predict by themselves the risk of rupture. Therefore, our hypothesis is that the risk of rupture lies on the combination of multiple actors. These actors together would play different roles that could be: weakening of the artery wall, increasing biomechanical stresses on the wall induced by blood flow, in addition to personal sensitivity due to family history, or personal history of comorbidity, or even seasonal variations that could gate different inflammation mechanisms.The main goal of this project is to identify relevant variables that may help in the process of predicting the risk of intracranial aneurysm rupture using machine learning and image processing techniques based on structured and non-structured data from multiple sources. We believe that the identification and the combined use of relevant variables extracted from clinical, demographical, environmental and medical imaging data sources will improve the estimation of the aneurysm rupture risk, with respect to the actual practiced method based essentially on the aneurysm size.The methodology of this work consist of four phases: (1) Data collection and storage, (2) feature extraction from multiple sources in particular from angiographic images, (3) development of the model that could describe the risk of aneurysm rupture based on the fusion and combination of the ...

Cerebral Aneurysm Detection and Analysis Challenge 2020 (CADA)

“…Other published solutions [28,32,35,36,[38][39][40][41][42] illustrate slightly better accuracy than the proposed methods for medium-or larger-sized aneurysms, with an accuracy maxing at around 0.8. Additional clinical parameters like smoking or hypertension [43,44] might improve the accuracy Fig. 7.…”

“…The submitted results on the recognition and segmentation sub-challenges present solutions whose performance is similar to that of human experts. State-of-theart methods [28][29][30][31][32][33][34][35][36][37]44] typically utilize a combination of morphological and CFD features. The top performing solutions submitted to the CADA challenge utilize similar features in combination with machine learning approaches to predict the risk of rupture.…”

Intracranial Aneurysm Rupture Risk Estimation Utilizing Vessel-Graphs and Machine Learning

“…Smoking is a crucial factor in predicting the rupture of cerebral aneurysms [2,3]. Patient-specific parameters like age, sex, and hypertension are additional factors in the rupture prediction [3].…”

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“…Furthermore, machine learning method has also been used in the image feature extraction process. Some research on this has been done in [17][18][19][20][21][22]. However, both the holistic and local methods can also be used to construct an image texture.…”