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About

João P. Monteiro holds an MSc degree in Biomedical Engineering from the University of Porto. He is currently doing his PhD and working at the Visual Computing and Machine Intelligence Group within INESC TEC in Porto. His PhD topic is personal health systems for assessment of upper extremity impairments. His main research interests are computer vision, machine learning and medical decision support systems.

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Details

Details

004
Publications

2018

Three-dimensional planning tool for breast conserving surgery: A technological review

Authors
Oliveira, SP; Morgado, P; Gouveia, PF; Teixeira, JF; Bessa, S; Monteiro, JP; Zolfagharnasab, H; Reis, M; Silva, NL; Veiga, D; Cardoso, MJ; Oliveira, HP; Ferreira, MJ;

Publication
Critical Reviews in Biomedical Engineering

Abstract
Breast cancer is one of the most common malignanciesaffecting women worldwide. However, despite its incidence trends have increased, the mortality rate has significantly decreased. The primary concern in any cancer treatment is the oncological outcome but, in the case of breast cancer, the surgery aesthetic result has become an important quality indicator for breast cancer patients. In this sense, an adequate surgical planning and prediction tool would empower the patient regarding the treatment decision process, enabling a better communication between the surgeon and the patient and a better understanding of the impact of each surgical option. To develop such tool, it is necessary to create complete 3D model of the breast, integrating both inner and outer breast data. In this review, we thoroughly explore and review the major existing works that address, directly or not, the technical challenges involved in the development of a 3D software planning tool in the field of breast conserving surgery. © 2018 by Begell House, Inc.

2017

Multi-modal Complete Breast Segmentation

Authors
Zolfagharnasab, H; Monteiro, JP; Teixeira, JF; Borlinhas, F; Oliveira, HP;

Publication
Pattern Recognition and Image Analysis - 8th Iberian Conference, IbPRIA 2017, Faro, Portugal, June 20-23, 2017, Proceedings

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. © Springer International Publishing AG 2017.

2016

Cognition inspired format for the expression of computer vision metadata

Authors
Castro, H; Monteiro, J; Pereira, A; Silva, D; Coelho, G; Carvalho, P;

Publication
MULTIMEDIA TOOLS AND APPLICATIONS

Abstract
Over the last decade noticeable progress has occurred in automated computer interpretation of visual information. Computers running artificial intelligence algorithms are growingly capable of extracting perceptual and semantic information from images, and registering it as metadata. There is also a growing body of manually produced image annotation data. All of this data is of great importance for scientific purposes as well as for commercial applications. Optimizing the usefulness of this, manually or automatically produced, information implies its precise and adequate expression at its different logical levels, making it easily accessible, manipulable and shareable. It also implies the development of associated manipulating tools. However, the expression and manipulation of computer vision results has received less attention than the actual extraction of such results. Hence, it has experienced a smaller advance. Existing metadata tools are poorly structured, in logical terms, as they intermix the declaration of visual detections with that of the observed entities, events and comprising context. This poor structuring renders such tools rigid, limited and cumbersome to use. Moreover, they are unprepared to deal with more advanced situations, such as the coherent expression of the information extracted from, or annotated onto, multi-view video resources. The work here presented comprises the specification of an advanced XML based syntax for the expression and processing of Computer Vision relevant metadata. This proposal takes inspiration from the natural cognition process for the adequate expression of the information, with a particular focus on scenarios of varying numbers of sensory devices, notably, multi-view video.

2016

Breast Conserving Surgery Outcome Prediction: A Patient-Specific, Integrated Multi-modal Imaging and Mechano-Biological Modelling Framework

Authors
Eiben, B; Lacher, R; Vavourakis, V; Hipwell, JH; Stoyanov, D; Williams, NR; Sabczynski, J; Buelow, T; Kutra, D; Meetz, K; Young, S; Barschdorf, H; Oliveira, HP; Cardoso, JS; Monteiro, JP; Zolfagharnasab, H; Sinkus, R; Gouveia, P; Liefers, GJ; Molenkamp, B; van de Velde, CJH; Hawkes, DJ; Cardoso, MJ; Keshtgar, M;

Publication
BREAST IMAGING, IWDM 2016

Abstract
Patient-specific surgical predictions of Breast Conserving Therapy, through mechano-biological simulations, could inform the shared decision making process between clinicians and patients by enabling the impact of different surgical options to be visualised. We present an overview of our processing workflow that integrates MR images and three dimensional optical surface scans into a personalised model. Utilising an interactively generated surgical plan, a multi-scale open source finite element solver is employed to simulate breast deformity based on interrelated physiological and biomechanical processes that occur post surgery. Our outcome predictions, based on the pre-surgical imaging, were validated by comparing the simulated outcome with follow-up surface scans of four patients acquired 6 to 12 months post-surgery. A mean absolute surface distance of 3.3mm between the follow-up scan and the simulation was obtained.

2014

A depth-map approach for automatic mice behavior recognition

Authors
Monteiro, JP; Oliveira, HP; Aguiar, P; Cardoso, JS;

Publication
2014 IEEE International Conference on Image Processing, ICIP 2014, Paris, France, October 27-30, 2014

Abstract
Animal behavior assessment plays an important role in basic and clinical neuroscience. Although assessing the higher functional level of the nervous system is already possible, behavioral tests are extremely complex to design and analyze. Animal's responses are often evaluated manually, making it subjective, extremely time consuming, poorly reproducible and potentially fallible. The main goal of the present work is to evaluate the use of consumer depth cameras, such as the Microsoft's Kinect, for detection of behavioral patterns of mice. The hypothesis is that the depth information, should enable a more feasible and robust method for automatic behavior recognition. Thus, we introduce our depth-map based approach comprising mouse segmentation, body-like per-frame feature extraction and per-frame classification given temporal context, to prove the usability of this methodology. © 2014 IEEE.