Abstract

Abstract
The deep inferior epigastric perforator (DIEP) is the most commonly used free flap in mastectomy reconstruction. Preoperative imaging techniques are routinely used to detect location, diameter and course of perforators, with direct intervention from the imaging team, who subsequently draw a chart that will help surgeons choosing the best vascular support for the reconstruction. In this work, the feasibility of using a computer software to support the preoperative planning of 40 patients proposed for breast reconstruction with a DIEP flap is evaluated for the first time. Blood vessel centreline extraction and local characterization algorithms are applied to identify perforators and compared with the manual mapping, aiming to reduce the time spent by the imaging team, as well as the inherent subjectivity to the task. Comparing with the measures taken during surgery, the software calibre estimates were worse for vessels smaller than 1.5 mm (P = 6e-4) but better for the remaining ones (P = 2e-3). Regarding vessel location, the vertical component of the software output was significantly different from the manual measure (P = 0.02), nonetheless that was irrelevant during surgery as errors in the order of 2-3 mm do not have impact in the dissection step. Our trials support that a reduction of the time spent is achievable using the automatic tool (about 2 h/case). The introduction of artificial intelligence in clinical practice intends to simplify the work of health professionals and to provide better outcomes to patients. This pilot study paves the way for a success story. (C) 2020 The Authors. Published by Elsevier Ltd.

Abstract

Abstract
Active contour models are one of the most emblematic algorithms of computer vision. Their strong theoretical foundations and high user interoperahility turned them into a reference approach for object segmentation and tracking tasks. A high number of modifications have already been proposed in order to overcome the known problems of traditional snakes, such as initialization dependence and poor convergence to concavities. In this paper, we address the scenario where the user wants to segment an object that has multiple dynamic regions but some of them do not correspond to the true object boundary. We propose a novel parametric active contour model, the Sparse Multi-Bending snake, which is capable of dividing the contour into a set of contiguous regions with different bending properties. We derive a new energy function that induces such behavior and presents a group optimization strategy that can be used to find the optimal bending resistance parameter for each point of the contour. We show the flexibility of our model in a set of synthetic images. In addition, we consider two real applications, lung segmentation in Computerized Tomography data and hand segmentation in depth images. We show how the proposed method is able to improve the segmentations obtained in both applications, when compared with other active contour models.

Abstract
The segmentation of retinal vessels in fundus images has been heavily focused in the past years, given their relevance in the diagnosis of several health conditions. Even though the recent advent of deep learning allowed to foster the performance of computer-based algorithms in this task, further improvement concerning the detection of vessels while suppressing background noise has clinical significance. Moreover, the best performing state-of-the-art methodologies conduct patch-based predictions. This, put together with the preprocessing techniques used in those methodologies, may hinder their use in screening scenarios. Thus, in this paper, we explore a fully convolutional setting that takes raw fundus images and allows to combine patch-based training with global image prediction. Our experiments on the DRIVE, STARE and CHASEDB1 databases show that the proposed methodology achieves state-of-the-art performance in the first and the last, allowing at the same time much faster segmentation of new images.