The assessment of vascular complexity in the lower limbs provides important information about peripheral artery diseases, with a relevant impact on both therapeutic decisions and on prognostic estimation. Currently, the evaluation is carried out by visual inspection of cine-angiograms, which is largely operator-dependent. An automatic image analysis could offer a fast and more reliable technique to support physicians with the clinical management of these patients. In this work, we introduce a new method to automatically segment the vascular tree from cine-angiography images, in order to improve the clinical interpretation of the complexity of vascular collaterals in Peripheral Arterial Occlusive Disease (PAOD) patients. The approach is based on: (1) a feature-detection method to convert the video into a static image with lager Field Of View (FOV) and (2) a custom Convolutional Neural Network (CNN) for the segmentation of vascular structure. Experimental evaluations over a set of clinical cases confirm the viability of the approach: accuracy is assessed in terms of area under the ROC curve, where an average value of 0.988 ± 0.006 is measured.

Using CNNs for Designing and Implementing an Automatic Vascular Segmentation Method of Biomedical Images

Bruno P.
;
Zaffino P.;De Rosa S.;Calimeri F.
;
2018

Abstract

The assessment of vascular complexity in the lower limbs provides important information about peripheral artery diseases, with a relevant impact on both therapeutic decisions and on prognostic estimation. Currently, the evaluation is carried out by visual inspection of cine-angiograms, which is largely operator-dependent. An automatic image analysis could offer a fast and more reliable technique to support physicians with the clinical management of these patients. In this work, we introduce a new method to automatically segment the vascular tree from cine-angiography images, in order to improve the clinical interpretation of the complexity of vascular collaterals in Peripheral Arterial Occlusive Disease (PAOD) patients. The approach is based on: (1) a feature-detection method to convert the video into a static image with lager Field Of View (FOV) and (2) a custom Convolutional Neural Network (CNN) for the segmentation of vascular structure. Experimental evaluations over a set of clinical cases confirm the viability of the approach: accuracy is assessed in terms of area under the ROC curve, where an average value of 0.988 ± 0.006 is measured.
978-3-030-03839-7
978-3-030-03840-3
Bioinformatics; Biomedical imaging; Cine-angiography; Convolutional Neural Networks; Feature detection; Segmentation
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/20.500.11770/299168
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