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
Zaffino P.;Scaramuzzino S.;De Rosa S.;Indolfi C.;Calimeri F.;Spadea M. F.
2018-01-01
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.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.