| Author | Silva, Jefferson | |
| Author | Souza, Luiz | |
| Author | Chagas, Paulo | |
| Author | Calumby, Rodrigo | |
| Author | Souza, Bianca | |
| Author | Pontes, Izabelle | |
| Author | Duarte, Angelo | |
| Author | Pinheiro, Nathanael | |
| Author | Santos, Washington | |
| Author | Oliveira, Luciano | |
| Access date | 2022-12-22T15:59:03Z | |
| Available date | 2022-12-22T15:59:03Z | |
| Document date | 2022 | |
| Citation | SILVA, Jefferson et al. Boundary-aware glomerulus segmentation: toward one-to-many stain generalization. Computerized Medical Imaging and Graphics, v. 100, p. 1-13, 2022. | en_US |
| ISSN | 1879-0771 | en_US |
| URI | https://www.arca.fiocruz.br/handle/icict/56167 | |
| Sponsorship | Fundação de Amparo à Pesquisa do Estado da Bahia (FAPESB).
Inova FIOCRUZ.
Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq). | en_US |
| Language | eng | en_US |
| Publisher | Elsevier | en_US |
| Rights | open access | en_US |
| Subject in Portuguese | Rim | en_US |
| Subject in Portuguese | Segmentação | en_US |
| Subject in Portuguese | Molecular | en_US |
| Subject in Portuguese | Imagem celular | en_US |
| Subject in Portuguese | Aprendizagem de ponta a ponta em imagens médicas | en_US |
| Title | Boundary-aware glomerulus segmentation: toward one-to-many stain generalization | en_US |
| Type | Article | en_US |
| DOI | 10.1016/j.compmedimag.2022.102104 | |
| Abstract | The growing availability of scanned whole-slide images (WSIs) has allowed nephropathology to open new possibilities for medical decision-making over high-resolution images. Diagnosis of renal WSIs includes locating and identifying specific structures in the tissue. Considering the glomerulus as one of the first structures analyzed by pathologists, we propose here a novel convolutional neural network for glomerulus segmentation. Our end-to-end network, named DS-FNet, combines the strengths of semantic segmentation and semantic boundary detection networks via an attention-aware mechanism. Although we trained the proposed network on periodic acid-Schiff (PAS)-stained WSIs, we found that our network was capable to segment glomeruli on WSIs stained with different techniques, such as periodic acid-methenamine silver (PAMS), hematoxylin-eosin (HE), and Masson trichrome (TRI). To assess the performance of the proposed method, we used three public data sets: HuBMAP (available in a Kaggle competition), a subset of the NEPTUNE data set, and a novel challenging data set, called WSI_Fiocruz. We compared the DS-FNet with six other deep learning networks: original UNet, our attention version of U-Net called AU-Net, U-Net++, U-Net3Plus, ResU-Net, and DeepLabV3+. Results showed that DS-FNet achieved equivalent or superior results on all data sets: On the HuBMAP data set, it reached a dice score (DSC) of 95.05%, very close to the first place (95.15%); on the NEPTUNE and WSI_Fiocruz data sets, DS-FNet obtained the highest average DSC, whether on PAS-stained images or images stained with
other techniques. To the best we know, this is the first work to show consistently high performance in a one-to-many-stain glomerulus segmentation following a thorough protocol on data sets from different medical labs. | en_US |
| Affilliation | Universidade Federal do Maranhão, São Luís, MA, Brasil / Universidade Federal da Bahia. Salvador, BA, Brasil. | en_US |
| Affilliation | Universidade Federal da Bahia. Salvador, BA, Brasil. | en_US |
| Affilliation | Universidade Federal da Bahia. Salvador, BA, Brasil. | en_US |
| Affilliation | Universidade Estadual de Feira de Santana. Feira de Santana, BA, Brasil. | en_US |
| Affilliation | Universidade Federal da Bahia. Salvador, BA, Brasil /Fundação Oswaldo Cruz, Instituto Gonçalo Muniz. Salvador, BA, Brasil. | en_US |
| Affilliation | Universidade Federal da Bahia. Salvador, BA, Brasil. | en_US |
| Affilliation | Universidade Estadual de Feira de Santana. Feira de Santana, BA, Brasil. | en_US |
| Affilliation | Laboratório Imagepat, Salvador, BA, Brasil. | en_US |
| Affilliation | Universidade Federal da Bahia. Salvador, BA, Brasil / Fundação Oswaldo Cruz, Instituto Gonçalo Muniz. Salvador, BA, Brasil. | en_US |
| Affilliation | Universidade Federal da Bahia. Salvador, BA, Brasil. | en_US |
| Subject | Kidney | en_US |
| Subject | Segmentation | en_US |
| Subject | Molecular | en_US |
| Subject | Cellular imaging | en_US |
| Subject | End-to-end learning in medical imaging | en_US |
| DeCS | Rim | en_US |
| xmlui.metadata.dc.subject.ods | 04 Educação de qualidade | |
