C-BER - Indexed Articles in Conferences

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Browsing C-BER - Indexed Articles in Conferences by Author "7800"
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    Attention-driven Spatial Transformer Network for Abnormality Detection in Chest X-Ray Images
    ( 2022) Joana Maria Rocha ; Sofia Cardoso Pereira ; João Manuel Pedrosa ; Aurélio Campilho ; Ana Maria Mendonça ; 6071 ; 6381 ; 7623 ; 7800 ; 8251
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    Chest Radiography Few-Shot Image Synthesis for Automated Pathology Screening Applications
    ( 2021) Sousa,MQE ; João Manuel Pedrosa ; Joana Maria Rocha ; Sofia Cardoso Pereira ; Ana Maria Mendonça ; Aurélio Campilho ; 7800 ; 8251 ; 6071 ; 6381 ; 7623
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    Comparison of conventional and deep learning based methods for pulmonary nodule segmentation in CT images
    ( 2019) Joana Maria Rocha ; António Cunha ; Maria Mendonça,A ; 6271 ; 7800
    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.
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    Segmentation of COVID-19 Lesions in CT Images
    ( 2021) Joana Maria Rocha ; Sofia Cardoso Pereira ; Aurélio Campilho ; Ana Maria Mendonça ; 6071 ; 6381 ; 7800 ; 8251
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    Segmentation of Pulmonary Nodules in CT Images Using the Sliding Band Filter
    ( 2020) Joana Maria Rocha ; António Cunha ; Ana Maria Mendonça ; 6271 ; 6381 ; 7800
    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.