Automatic Segmentation of Diabetic foot ulcer from Mask Region-Based Convolutional Neural Networks

Abstract

Diabetic foot ulcer represents one of the leading causes of lower extremity amputation in diabetic patients. The aim of this work is to propose a computational method to carry out segmentation on diabetic foot ulcer images of patients treated with the Heberprot-P. This drug accelerates the wound healing process and reduces the risk of amputation. The used material is a bank of 1176 images provided by the Center for Genetic Engineering and Biotechnology at Havana, and as a method, we propose the use of the model of Mask R-CNN and the concept of learning transfer to automatically locate the region that delimits the ulcer. The proposed model obtained very satisfactory results, and we validated its performance together with specialist physicians, on a set of 1010 images.

SARS-CoV-2: theoretical analysis of the proposed algorithms to the enhancement and segmentation of high-resolution microscopy images—Part II

“…Due to the lack of truth images, the quantitative evaluation of enhancement and segmentation techniques is difficult to achieve. According to many authors, this remains an open problem [ 14 ]. For that reason, some quantitative comparison with manual segmentation may provide a useful indication.…”

“…Tables 2 and 3 list the results of comparisons of manually contrasted images with those obtained by our Algorithms and with the HPF and SLCE . We utilized as metrics of evaluation: Accuracy , Sensitivity , Precision , Specificity and F-Measure [ 14 – 16 ].…”

SARS-CoV-2: Theoretical analysis of the proposed algorithms to the Enhancement and Segmentation of High-Resolution Microscopy Images. Part II

“…Due to the lack of truth images, the quantitative evaluation of enhancement and segmentation techniques are difficult to achieve. According to many authors, this remains an open problem [15]. For that reason, some quantitative comparison with manual segmentation may provide a useful indication.…”

“…Table II list the results of comparisons of manually contrasted images with those obtained by our Algorithms, and with the HPF and SLCE. We utilized as metrics of evaluation: Accuracy, Sensitivity, Precision, Specificity and F-Measure [15], [16], [17]. Table I.…”

Recognition of ischaemia and infection in diabetic foot ulcer: A deep convolutional neural network based approach

“…However, sample images are taken from Japanese race patients only, therefore generalization of the usability of this approach was one of the prime limitations. In a study by Muñoz et al, 20 the authors proposed another segmentation approach based on Mask R-CNN. The DFU images they used were from patients who have been treated with Heberprot-P drug.…”