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About

About

I began my academic life in 2007 by joining the Bachelor of Computing Engineering and Medical Instrumentation, at ISEP. After concluding my bachelor degree in 2010, I’ve joined the master degree in biomedical engineering, at FEUP. There I worked under the supervision of Professor Aurélio Campilho and I developed an ultrasound classification system used to the detection of the carotid walls. After the conclusion of my master degree, I moved to Belgium where I did my PhD. In my PhD I’ve developed a system which allows clinicians to quantify the tissue deformation patterns of the Achilles tendon in-vivo.

Since March 2017 I've been collaborating with INESCTEC. I am working on C-BER where we have been developing a computer-aided diagnosis system for the prevention and early diagnosis of pathologies of the female reproductive system.

Interest
Topics
Details

Details

002
Publications

2019

Analysis of the performance of specialists and an automatic algorithm in retinal image quality assessment

Authors
Wanderley, DS; Araújo, T; Carvalho, CB; Maia, C; Penas, S; Carneiro, A; Mendonça, AM; Campilho, A;

Publication
2019 IEEE 6th Portuguese Meeting on Bioengineering (ENBENG)

Abstract

2019

End-to-end ovarian structures segmentation

Authors
Wanderley, DS; Carvalho, CB; Domingues, A; Peixoto, C; Pignatelli, D; Beires, J; Silva, J; Campilho, A;

Publication
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Abstract
The segmentation and characterization of the ovarian structures are important tasks in gynecological and reproductive medicine. Ultrasound imaging is typically used for the medical diagnosis within this field but the understanding of the images can be difficult due to their characteristics. Furthermore, the complexity of ultrasound data may lead to a heavy image processing, which makes the application of classical methods of computer vision difficult. This work presents the first supervised fully convolutional neural network (fCNN) for the automatic segmentation of ovarian structures in B-mode ultrasound images. Due to the small dataset available, only 57 images were used for training. In order to overcome this limitation, several regularization techniques were used and are discussed in this paper. The experiments show the ability of the fCNN to learn features to distinguish ovarian structures, achieving a Dice similarity coefficient (DSC) of 0.855 for the segmentation of the stroma and a DSC of 0.955 for the follicles. When compared with a semi-automatic commercial application for follicle segmentation, the proposed fCNN achieved an average improvement of 19%. © Springer Nature Switzerland AG 2019.

2019

End-to-End Ovarian Structures Segmentation

Authors
Wanderley, DS; Carvalho, CB; Domingues, A; Peixoto, C; Pignatelli, D; Beires, J; Silva, J; Campilho, A;

Publication
Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications - Lecture Notes in Computer Science

Abstract

2019

Deep Learning Approaches for Gynaecological Ultrasound Image Segmentation: A Radio-Frequency vs B-mode Comparison

Authors
Carvalho, C; Marques, S; Peixoto, C; Pignatelli, D; Beires, J; Silva, J; Campilho, A;

Publication
Lecture Notes in Computer Science - Image Analysis and Recognition

Abstract

2018

3D Tendon Strain Estimation Using High-frequency Volumetric Ultrasound Images: A Feasibility Study

Authors
Carvalho, C; Slagmolen, P; Bogaerts, S; Scheys, L; D'hooge, J; Peers, K; Maes, F; Suetens, P;

Publication
Ultrasonic Imaging

Abstract