Banca de DEFESA: JEFFERSON FONTINELE DA SILVA
Uma banca de DEFESA de DOUTORADO foi cadastrada pelo programa.
DISCENTE : JEFFERSON FONTINELE DA SILVA
DATA : 20/12/2021
HORA: 14:00
LOCAL: meet.google.com/wnr-vqgk-agb
TÍTULO:
Paying attention to the boundaries in semantic image segmentation
PALAVRAS-CHAVES:
image segmentation, image semantic segmentation, convolutional network, alpha expansion.
PÁGINAS: 80
RESUMO:
Image segmentation consists of assigning a label to each pixel in the image in such a way that pixels belonging to the same objects in the image must have the same labels. The segmented area of an object must span all pixels up to the limits (boundaries) with the other objects. The boundary region can provide helpful information for the segmentation process, as it marks a discontinuity that can define a segment limit. However, segmentation methods commonly suffer to explore boundary information and consequently to segment this region. This is so mainly due to the proximity between image regions containing different labels. In view of that, we propose to investigate how to take into account the boundary information when semantically segmenting an image object. Our first contribution is a graph-based image segmenter, called interactive dynamic programming (IDP)-expansion. This is a weakly-supervised method that requires a seed into each object targeted to be segmented in the image, subsequently minimizing an energy function to obtain the image labels. IDP-expansion explores dynamic programming to initialize an alpha expansion algorithm over superpixels to improve boundary information in a segmentation process. Over the Berkeley segmentation data set, our experiments showed that IDP-expansion is 51.2% faster than a traditional alpha-expansion based segmentation. Although IDP-expansion has shown to be faster, it suffers from two matters: A mandatory seed initialization and the lack of semantic information. This further led us to develop a supervised convolutional neural network architecture to semantically explore boundary information. Our novel method, called DS-FNet, uses two streams integrated in an end-to-end convolutional network to combine segmentation and boundary information based on an attention-aware mechanism. To evaluate DS-FNet, we initially conducted experiments on general-purpose (Pascal Context) and traffic (Cityscapes, CamVid, and Mapillary Vistas) image data sets, having the mean intersection over union (mIoU) as the reference metric. DS-FNet outperformed ten segmentation networks in the Pascal Context, Cityscapes, and CamVid data sets. In the Mapillary Vistas data set, DS-FNet achieved second place when compared to five other methods. A second round of experiments was performed to evaluate the generalization of our proposed method on challenging medical imaging data sets, containing several kidney biopsy whole slide images (WSIs). The data sets used to evaluate the second version of our network were HubMAP, WSI Fiocruz, and a subset of Neptune data set, all considering glomerulus segmentation. After training DS-FNet only over HubMAP data set, containing periodic acid-Schiff (PAS)-stained WSIs with only non-injured glomeruli, we found that our network was capable to segment glomeruli on WSIs stained by other methods (hematoxylin-eosin (HE), periodic acid-methenamine silver (PAMS), trichrome (TRI), and silver (SIL)). The results of these latter experiments show that our model is more robust than other models based on U-Net architecture. All the experiments and analyses presented in this work demonstrated that the explicit and adequate consideration of boundary information improves the results over non-boundary segmentation methods.
MEMBROS DA BANCA:
Presidente - 1914064 - LUCIANO REBOUCAS DE OLIVEIRA
Externa à Instituição - MICHELE FÚLVIA ANGELO - UEFS
Externo à Instituição - ALEXANDRE DA COSTA E SILVA FRANCO - IFBA
Externo à Instituição - FLAVIO DE BARROS VIDAL
Externo à Instituição - RICARDO DA SILVA TORRES