Abstract
Breast cancer imaging research has seen continuous progress throughout the years. Innovative visualization tools and easier planning techniques are being developed. Image segmentation methodologies generally have best results when applied to specific types of exams or sequences, as their features enhance and expedite those approaches. Particular methods have more purchase with the segmentation of particular structures. This is the case with diverse breast structures and the respective lesions on MRI sequences, over T1w and Dyn. The present study presents a methodology to tackle an unapproached task. We aim to facilitate the volumetric alignment of data retrieved from T1w and Dyn sequences, leveraging breast surface segmentation and registration. The proposed method revolves around Canny edge detection and mending potential holes on the surface, in order to accurately reproduce the breast shape. The contour is refined with a Level-set approach and the surfaces are aligned together using a restriction of the Iterative Closest Point (ICP) method. This could easily be applied to other paired same-time, volumetric sequences. The process seems to have promising results as average two-dimensional contour distances are at sub-voxel resolution and visual results seem well within range for the valid transference of other segmented or annotated structures. © Springer Nature Switzerland AG 2020.

Abstract

Abstract
The adherence to inhaled controller medications is of critical importance to achieve good clinical results in patients with chronic respiratory diseases. To objectively verify the adherence, a detection tool was previously developed and integrated in the mobile application InspirerMundi, based on image processing methods. In this work, a new approach for enhanced adherence verification was developed. In a first phase template matching is employed to confirm the inhaler positioning and to locate the dose counter. In a second phase Google ML Kit framework is used for the detection of each numerical dose in the dose counter. The proposed approach was validated through a new detection tool pilot implementation, using a set of images collected by patients using the application in their daily life. Performance of each of the two phases was evaluated for a set of commonly used inhaler devices. Promising results were achieved showing the potential of mobile embedded sensors without the need for external devices.

Abstract
Smart device microphone spirometry, based on the audio recording of forced expiratory maneuver (FEM), can be a simple, ubiquitous and easy tool for patients to self-monitor their asthma. Automatic validity assessment is crucial to guarantee that the global effort of the FEM fulfil the admissible minimum or if the maneuver needs to be repeated. In this work an automatic method to classify the sounds from FEM with respect to global effort was developed and evaluated using data from 54 children (5-10 years). The method proposed was able to correctly classify the microphone spirometry with respect to admissible minimum of effort with an accuracy of 86% (specificity 87% and sensitivity 86%). This method can be used to provide immediate feedback of the correct execution of the maneuver, improving the clinical value and utility of this self-monitoring tool.

Abstract
The scarcity of balanced and annotated datasets has been a recurring problem in medical image analysis. Several researchers have tried to fill this gap employing dataset synthesis with adversarial networks (GANs). Breast magnetic resonance imaging (MRI) provides complex, texture-rich medical images, with the same annotation shortage issues, for which, to the best of our knowledge, no previous work tried synthesizing data. Within this context, our work addresses the problem of synthesizing breast MRI images from corresponding annotations and evaluate the impact of this data augmentation strategy on a semantic segmentation task. We explored variations of image-to-image translation using conditional GANs, namely fitting the generator's architecture with residual blocks and experimenting with cycle consistency approaches. We studied the impact of these changes on visual verisimilarity and how an U-Net segmentation model is affected by the usage of synthetic data. We achieved sufficiently realistic-looking breast MRI images and maintained a stable segmentation score even when completely replacing the dataset with the synthetic set. Our results were promising, especially when concerning to Pix2PixHD and Residual CycleGAN architectures.

Abstract
Introduction: Breast volume estimation is considered crucial for breast cancer surgery planning. A single, easy, and reproducible method to estimate breast volume is not available. This study aims to evaluate, in patients proposed for mastectomy, the accuracy of the calculation of breast volume from a low-cost 3D surface scan (Microsoft Kinect) compared to the breast MRI and water displacement technique. Material and Methods: Patients with a Tis/T1-T3 breast cancer proposed for mastectomy between July 2015 and March 2017 were assessed for inclusion in the study. Breast volume calculations were performed using a 3D surface scan and the breast MRI and water displacement technique. Agreement between volumes obtained with both methods was assessed with the Spearman and Pearson correlation coefficients. Results: Eighteen patients with invasive breast cancer were included in the study and submitted to mastectomy. The level of agreement of the 3D breast volume compared to surgical specimens and breast MRI volumes was evaluated. For mastectomy specimen volume, an average (standard deviation) of 0.823 (0.027) and 0.875 (0.026) was obtained for the Pearson and Spearman correlations, respectively. With respect to MRI annotation, we obtained 0.828 (0.038) and 0.715 (0.018). Discussion: Although values obtained by both methodologies still differ, the strong linear correlation coefficient suggests that 3D breast volume measurement using a low-cost surface scan device is feasible and can approximate both the MRI breast volume and mastectomy specimen with sufficient accuracy. Conclusion: 3D breast volume measurement using a depth-sensor low-cost surface scan device is feasible and can parallel MRI breast and mastectomy specimen volumes with enough accuracy. Differences between methods need further development to reach clinical applicability. A possible approach could be the fusion of breast MRI and the 3D surface scan to harmonize anatomic limits and improve volume delimitation.