Abstract
We present a hierarchical harmonic mixing method for assisting users in the process of music mashup creation. Our main contributions are metrics for computing the harmonic compatibility between musical audio tracks at small- and large-scale structural levels, which combine and reassess existing perceptual relatedness (i.e., chroma vector similarity and key affinity) and dissonance-based approaches. Underpinning our harmonic compatibility metrics are harmonic indicators from the perceptually-motivated Tonal Interval Space, which we adapt to describe musical audio. An interactive visualization shows hierarchical harmonic compatibility viewpoints across all tracks in a large musical audio collection. An evaluation of our harmonic mixing method shows our adaption of the Tonal Interval Space robustly describes harmonic attributes of musical instrument sounds irrespective of timbral differences and demonstrates that the harmonic compatibility metrics comply with the principles embodied in Western tonal harmony to a greater extent than previous approaches. © 2018, Springer Nature Switzerland AG.

Abstract
Automatic segmentation of breast is an important step in the context of providing a planning tool for breast cancer conservative treatment, being important to segment completely the breast region in an objective way; however, current methodologies need user interaction or detect breast contour partially. In this paper, we propose a methodology to detect the complete breast contour, including the pectoral muscle, using multi-modality data. Exterior contour is obtained from 3D reconstructed data acquired from low-cost RGB-D sensors, and the interior contour (pectoral muscle) is obtained from Magnetic Resonance Imaging (MRI) data. Quantitative evaluation indicates that the proposed methodology performs an acceptable detection of breast contour, which is also confirmed by visual evaluation.

Abstract
The segmentation of the anterior fascia of the rectus abdominis muscle is an important step towards the analysis of abdominal vasculature. It may advance Computer Aided Detection tools that support the activity of clinicians who study vessels for breast reconstruction using the Deep Inferior Epigastric Perforator flap. In this paper, we propose a two-fold methodology to detect the anterior fascia in Computerized Tomographic Angiography volumes. First, a slice-wise thresholding is applied and followed by a post-processing phase. Finally, an interpolation framework is used to obtain a final smooth fascia detection. We evaluated our method in 20 different volumes, by calculating the mean Euclidean distance to manual annotations, achieving subvoxel error.

Abstract
Magnetic Resonance Imaging (MRI) exams suffer from undesirable structure replicating and overlapping effects on certain acquisition settings. These are called Spatial Aliasing Artefacts (SAA) and their presence interferes with the segmentation of other anatomical structures. This paper addresses the segmentation of the SAA in T1-weighted MRI image sets, in order to effectively remove their influence over the legitimately positioned body structures. The proposed method comprises an initial thresholding, employing the Triangle method, an aggregation of neighboring voxels through Region Growing. Further refinement of the objects contour is obtained with Convex Hull and a Minimum Path algorithm applied to two orthogonal planes (Sagittal and Axial). Some experiments concerning the extension of the pipeline used are reported and the results seem promising. The average contour distance concerning the Ground Truth (GT) rounds 2.5mm and area metrics point out average overlaps above 64% with the GT. Some issues concerning the fusion between the output from the two planes are to be perfected. Nevertheless, the results seems sufficient to neutralize the influence of SAA and expedite the downstream anatomical segmentation tasks.

Abstract
The development of a three-dimensional (3D) planing tool for breast cancer surgery requires the existence of proper deformable models of the breast, with parameters that can be manipulated to obtain the desired shape. However, modelling breast is a challenging task due to the lack of physical landmarks that remain unchanged after deformation. In this paper, the fitting of a 3D point cloud of the breast to a parametric model suitable for surgery planning is investigated. Regression techniques were used to learn breast deformation functions from exemplar data, resulting in comprehensive models easy to manipulate by surgeons. New breast shapes are modelled by varying the type and degree of deformation of three common deformations: ptosis, turn and top-shape.

Abstract
Surgery planing of breast cancer interventions is gaining importance among physicians, who recognize value in discussing the possible aesthetic outcomes of surgery with patients. Research is been propelled to create patient-specific breast models, but breast image registration algorithms are still limited, particularly for the purpose of matching pre- and post-surgical data of patient's breast surfaces. Yet, this is a fundamental task to learn prediction models of breast healing process after surgery. In this paper, a coarse-to-fine registration strategy is proposed to match breast surface data acquired before and after surgery. Methods are evaluated in their ability to register surfaces in an anatomical reliable way, and results suggest proper alignment adequated to be used as input to train deformable models.