Abstract
Through the years, several CAD systems have been developed to help radiologists in the hard task of detecting signs of cancer in the numerous screening mammograms. A more recent trend includes the development of pre-CAD systems aiming at identifying normal mammograms instead of detecting suspicious ones. Normal breasts are screened-out from the process, leaving radiologists more time to focus on more difficult cases. In this work, a new approach for the identification of normal breasts is presented. Considering that even breasts with malignant findings are mostly constituted by normal tissue, the breast area is divided into blocks which are then compared pairwise. If all blocks are very similar, the breast is labelled as normal, and as suspicious otherwise. Features characterizing the pairwise block similarity and characterizing the intra-block pixel distribution are used to design a predictive method based on machine learning techniques. The proposed solution was applied on a real world screening setting composed by nearly 18000 mammograms. Results are similar to the more complex state of the art approaches by correctly identifying more than 20% of the normal mammograms. These results suggest the usefulness of the relative comparison instead of the absolute classification. When properly used, simple statistics can suffice to distinguish the clearly normal breasts.

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.

Abstract
Breast cancer treatments can have a negative impact on breast aesthetics, in case when surgery is intended to intersect tumor. For many years mastectomy was the only surgical option, but more recently breast conserving surgery (BCS) has been promoted as a liable alternative to treat cancer while preserving most part of the breast. However, there is still a significant number of BCS intervened patients who are unpleasant with the result of the treatment, which leads to self-image issues and emotional overloads. Surgeons recognize the value of a tool to predict the breast shape after BCS to facilitate surgeon/patient communication and allow more educated decisions; however, no such tool is available that is suited for clinical usage. These tools could serve as a way of visually sensing the aesthetic consequences of the treatment. In this research, it is intended to propose a methodology for predict the deformation after BCS by using machine learning techniques. Nonetheless, there is no appropriate dataset containing breast data before and after surgery in order to train a learning model. Therefore, an in-house semi-synthetic dataset is proposed to fulfill the requirement of this research. Using the proposed dataset, several learning methodologies were investigated, and promising outcomes are obtained.

Abstract
In the development of new regenerative medicine therapies for the treatment of central nervous system and spinal cord injuries, the identification of factors that inhibit or promote cell outgrowth in neurite outgrowth assays is fundamental, and the neurotrophic activity is commonly assessed based on the neurite/cell outgrowth. Neurites are projections from the cell body or the initial neurosphere and typically present low-contrast to background in phase contrast images. The extent of neurites is usually measured in a manual way and fluorescence images are the most used, generally requiring imunofluorescent staining. We present a novel automatic approach for the quantification of cell outgrowth from neurospheres, based on phase contrast and fluorescence images acquired from samples merely processed for DNA staining. Our approach detects the neurite/cell outgrowth, and its measures are in high agreement with the ones obtained manually. Furthermore, the image analysis time was reduced in more than 95% allowing the increase of the amount of data to be analyzed. © 2013 Springer-Verlag.

Abstract
In the development of new regenerative medicine therapies for the treatment of central nervous system and spinal cord injuries, the identification of factors that inhibit or promote cell outgrowth in neurite outgrowth assays is fundamental, and the neurotrophic activity is commonly assessed based on the neurite/cell outgrowth. Neurites are projections from the cell body or the initial neurosphere and typically present low-contrast to background in phase contrast images. The extent of neurites is usually measured in a manual way and fluorescence images are the most used, generally requiring imunofluorescent staining. We present a novel automatic approach for the quantification of cell outgrowth from neurospheres, based on phase contrast and fluorescence images acquired from samples merely processed for DNA staining. Our approach detects the neurite/cell outgrowth, and its measures are in high agreement with the ones obtained manually. Furthermore, the image analysis time was reduced in more than 95% allowing the increase of the amount of data to be analyzed.