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

Chabert, Stéren; Mardones, Tomás; Riveros, Rodrigo; Godoy, Maximiliano; Veloz, Alejandro; Salas, Rodrigo; Cox, Pablo

doi:10.3897/rio.3.e11731

Cited by 19 publications

(19 citation statements)

References 13 publications

(13 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…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.…”

Section: Discussionmentioning

confidence: 99%

“…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.…”

Section: Discussionmentioning

confidence: 99%

See 1 more Smart Citation

Cerebral Aneurysm Detection and Analysis Challenge 2020 (CADA)

Ivantsits

Goubergrits

Kuhnigk

et al. 2021

Lecture Notes in Computer Science

View full text Add to dashboard Cite

Rupture of an intracranial aneurysm often results in subarachnoid hemorrhage, a life-threatening condition with high mortality and morbidity. The Cerebral Aneurysm Detection and Analysis (CADA) competition was organized to support the development and benchmarking of algorithms for the detection, analysis, and risk assessment of cerebral aneurysms in X-ray rotational angiography (3DRA) images. 109 anonymized 3DRA datasets were provided for training, and 22 additional datasets were used to test the algorithmic solutions. Cerebral aneurysm detection was assessed using the F2 score based on recall and precision, and the fit of the delivered bounding box was assessed using the distance to the aneurysm. Segmentation quality was measured using Jaccard and a combination of different surface distance measurements. Systematic errors were analyzed using volume correlation and bias. Rupture risk assessment was evaluated using the F2 score. 158 participants from 22 countries registered for the CADAchallenge. The detection solutions presented by the community are mostly accurate (F2 score 0.92) with a small number of missed aneurysms with diameters of 3.5 mm. In addition, the delineation of these structures is very good with a Jaccard score of 0.915. The rupture risk estimation methods achieved an F2 score of 0.7. The performance of the detection and segmentation solutions is equivalent to that of human experts. In rupture risk estimation, the best results are obtained by combining different image-based, morphological and computational fluid dynamic parameters using machine learning methods.

show abstract

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Cerebral Aneurysm Detection and Analysis Challenge 2020 (CADA)

Ivantsits

Goubergrits

Kuhnigk

et al. 2021

Lecture Notes in Computer Science

View full text Add to dashboard Cite

show abstract

“…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].…”

Section: Introductionmentioning

confidence: 99%

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

Ivantsits

Huellebrand

Kelle

et al. 2021

Cerebral Aneurysm Detection

View full text Add to dashboard Cite

Intracranial aneurysms frequently cause subarachnoid hemorrhage-a life-threatening condition with a high mortality and morbidity rate. State-of-the-art methods combine demographic, clinical, morphological, and computational fluid dynamics parameters.We propose a method combining morphological radiomics features, gray-level radiomics features, and a novel aneurysm site location encoding via directed graphs on the vessel tree. Some of the gray-level features seem to be good proxies for blood flow within the vessel and the aneurysms. Furthermore, our proposed method shows improved F2scores and accuracy across various models fed with the aneurysm site encoding. A K-nearest neighbors method shows the best results during our model selection with an F2-score of 0.7 and an accuracy of 0.73 on the relatively small private test set with 22 individuals and 30 aneurysms.

show abstract

“…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.…”

Section: Introductionmentioning

confidence: 99%

The new proposed method for texture modification of closed up face image based on image processing using local weighting pattern (LWP) with enhancement technique

Gaffar¹,

Shyafary²,

Rony³

et al. 2018

IJET

View full text Add to dashboard Cite

The texture is a two-and three-dimensional design element that is distinguished by the visual and physical properties perceived. Textured areas in the image can be marked with uniform or varying spatial intensity distribution. There are many techniques and methods from simple to sophisticated which available including machine learning-based methods to modify the texture map. The texture feature description becomes a new challenge in the field of computer vision and pattern recognition since the emergence of the local pattern binary method (LBP). This study proposes a new method called Local Weighting Pattern (LWP) for modifying textures based on the pixel's neighborhood of an RGB image. The results of this study obtained that LWP method produces a texture with a unique and artistic visualization. The Log function has been used to improve the image quality of the LWP method.

show abstract

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

Cited by 19 publications

References 13 publications

Cerebral Aneurysm Detection and Analysis Challenge 2020 (CADA)

Cerebral Aneurysm Detection and Analysis Challenge 2020 (CADA)

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

The new proposed method for texture modification of closed up face image based on image processing using local weighting pattern (LWP) with enhancement technique

Contact Info

Product

Resources

About