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About

About

Joana Rocha started her Integrated Master's in Bioengineering at the University of Porto in 2014, focusing on computer vision and artificial intelligence for biomedical applications. She joined Swansea University to study human motion patterns, developing an automated measurement technique for physical activity assessment. In 2018, she joined INESC-TEC, where she worked on a computer-aided diagnosis system for lung cancer, biometrics for presentation attack detection, and is now working on explainable AI for automated thoracic pathology screening.

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001
Publications

2020

Conventional Filtering Versus U-Net Based Models for Pulmonary Nodule Segmentation in CT Images

Authors
Rocha, J; Cunha, A; Mendonca, AM;

Publication
Journal of Medical Systems

Abstract

2020

Segmentation of Pulmonary Nodules in CT Images Using the Sliding Band Filter

Authors
Rocha, J; Cunha, A; Mendonca, AM;

Publication
IFMBE Proceedings

Abstract
This paper proposes a conventional approach for pulmonary nodule segmentation, that uses the Sliding Band Filter to estimate the center of the nodule, and consequently the filter’s support points, matching the initial border coordinates. This preliminary segmentation is then refined to try to include mainly the nodular area, and no other regions (e.g. vessels and pleural wall). The algorithm was tested on 2653 nodules from the LIDC database and achieved a Dice score of 0.663, yielding similar results to the ground truth reference, and thus being a promising tool to promote early lung cancer screening and improve nodule characterization. © 2020, Springer Nature Switzerland AG.

2020

Activity Mapping of Children in Play Using Multivariate Analysis of Movement Events

Authors
Rocha, JN; Barnes, CM; Rees, P; Clark, CT; Stratton, G; Summers, HD;

Publication
MEDICINE AND SCIENCE IN SPORTS AND EXERCISE

Abstract
Purpose (i) To develop an automated measurement technique for the assessment of both the form and intensity of physical activity undertaken by children during play. (ii) To profile the varying activity across a cohort of children using a multivariate analysis of their movement patterns. Methods Ankle-worn accelerometers were used to record 40 min of activity during a school recess, for 24 children over five consecutive days. Activity events of 1.1 s duration were identified within the acceleration time trace and compared with a reference motif, consisting of a single walking stride acceleration trace, obtained on a treadmill operating at a speed of 4 km h(-1). Dynamic time warping of motif and activity events provided metrics of comparative movement duration and intensity, which formed the data set for multivariate mapping of the cohort activity using a principal component analysis (PCA). Results The two-dimensional PCA plot provided clear differentiation of children displaying diverse activity profiles and clustering of those with similar movement patterns. The first component of the PCA correlated to the integrated intensity of movement over the 40-min period, whereas the second component informed on the temporal phasing of activity. Conclusions By defining movement events and then quantifying them by reference to a motion-standard, meaningful assessment of highly varied activity within free play can be obtained. This allows detailed profiling of individual children's activity and provides an insight on social aspects of play through identification of matched activity time profiles for children participating in conjoined play.

2019

Comparison of conventional and deep learning based methods for pulmonary nodule segmentation in CT images

Authors
Rocha, J; Cunha, A; Maria Mendonça, A;

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

Abstract
Lung cancer is among the deadliest diseases in the world. The detection and characterization of pulmonary nodules are crucial for an accurate diagnosis, which is of vital importance to increase the patients’ survival rates. The segmentation process contributes to the mentioned characterization, but faces several challenges, due to the diversity in nodular shape, size, and texture, as well as the presence of adjacent structures. This paper proposes two methods for pulmonary nodule segmentation in Computed Tomography (CT) scans. First, a conventional approach which applies the Sliding Band Filter (SBF) to estimate the center of the nodule, and consequently the filter’s support points, matching the initial border coordinates. This preliminary segmentation is then refined to include mainly the nodular area, and no other regions (e.g. vessels and pleural wall). The second approach is based on Deep Learning, using the U-Net to achieve the same goal. This work compares both performances, and consequently identifies which one is the most promising tool to promote early lung cancer screening and improve nodule characterization. Both methodologies used 2653 nodules from the LIDC database: the SBF based one achieved a Dice score of 0.663, while the U-Net achieved 0.830, yielding more similar results to the ground truth reference annotated by specialists, and thus being a more reliable approach. © Springer Nature Switzerland AG 2019.