Segmentação de Núcleos em Imagens Histológicas Renais
Abstract
This paper proposes a computer-aided diagnosis tool for kidney-related pathologies. The proposed system aims to perform the segmentation of cell nuclei in renal biopsy images, serving the diagnosis of several associated pathologies. The tests were performed on 122 images treated with Hematoxylin-Eosin (H&E) and Periodic Acid Schiff (PAS) dyes, sampled from two databases. The method included color deconvolution, morphological, and thresholding operations. A sample of segmented images was evaluated by an expert, who considered 73% of the segmentations as good or moderate.
References
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Bel, T., Hermsen, M., Smeets, B., Hilbrands, L., van der Laak, J., and Litjens, G. (2018). Automatic segmentation of histopathological slides of renal tissue using deep learning. In Medical Imaging 2018: Digital Pathology, volume 10581, page 1058112. International Society for Optics and Photonics.
Doi and Kunio (2007). Computer-aided diagnosis in medical imaging: historical review, current status and future potential. Computerized medical imaging and graphics, 31(4-5):198–211.
Govind, D., Ginley, B., Lutnick, B., Tomaszewski, J. E., and Sarder, P. (2018). Glomerular detection and segmentation from multimodal microscopy images using a butterworth band-pass filter. In Medical Imaging 2018: Digital Pathology, volume 10581, page 1058114. International Society for Optics and Photonics.
Ruifrok, A. C., Johnston, D. A., et al. (2001). Quantification of histochemical staining by color deconvolution. Analytical and quantitative cytology and histology, 23(4):291–299.
SBN (2019). Compreendendo os rins - sociedade brasileira de nefrologia. https://sbn.org.br/publico/institucional/compreendendo-os-rins/. Acesso em: 08/08/2019.
Sheehan, S. M. and Korstanje, R. (2018). Automatic glomerular identification and quantification of histological phenotypes using image analysis and machine learning. American Journal of Physiology-Renal Physiology, 315(6):F1644–F1651.
Tey, W. K., Kuang, Y. C., Khoo, J. J., Ooi, M. P.-L., and Demidenko, S. (2017). Automatic renal interstitial fibrosis quantification system. In 2017 IEEE International Instrumentation and Measurement Technology Conference (I2MTC), pages 1–6. IEEE.
Published
2019-12-26
How to Cite
BATISTA, Rodrigo E. C.; VERAS, Rodrigo M. S.; SANTOS, Justino D. ; SILVA, Huria S. M. C. .
Segmentação de Núcleos em Imagens Histológicas Renais. In: REGIONAL SCHOOL OF APPLIED COMPUTING FOR HEALTH (ERCAS), 7. , 2019, Teresina.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2019
.
p. 67-72.
