Ferramenta para auxílio na rotulação de datasets para segmentação semântica
Resumo
Este estudo tem como objetivo desenvolver uma ferramenta que auxilie na anotação de imagens para fins de segmentação, utilizando o SAM. Para alcançar esse propósito, optou-se por utilizar a linguagem de programação Python, que é a linguagem padrão do SAM. O ambiente de desenvolvimento e teste escolhido foi o Google Colab. Para validar a ferramenta desenvolvida, foram selecionados dois conjuntos de imagens. O primeiro é o dataset Kvasir-SEG, que contém 1000 imagens de pólipos obtidas de exames de colonoscopia. O segundo conjunto consiste em imagens de alfaces cultivadas em sistema hidropônico. Como resultado, a ferramenta obteve bom desempenho em ambos os conjuntos de imagens, demostrando ter potencial no auxílio de anotações manuais para segmentação de imagens.
Palavras-chave:
segmentação de imagens, anotação de imagens, aprendizado profundo
Referências
T. C. d. M. Nascimento, “Segmentação de imagens utilizando elementos de morfologia matemática,” Recife, Abril 2013, - Universidade Federal de Pernambuco, Dissertação de Mestrado.
R. C. Gonzalez and R. E. Woods, “Processamento digital de imagens,” vol. 3 ed., 2009.
M. F. S. Saldanha and C. C. Freitas, “Segmentação de imagens digitais: Uma revisão,” in Anais do 9º Workshop dos Cursos de Computação Aplicada do INPE. São José dos Campos: INPE, 2009. [Online]. Available: [link]
S. Rajalingappaa, Deep Learning for Computer Vision. Birmingham, UK: Packt Publishing, Jan. 2018.
S. Minaee, Y. Boykov, F. Porikli, A. Plaza, N. Kehtarnavaz, and D. Terzopoulos, “Image segmentation using deep learning: A survey,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 44, no. 7, pp. 3523–3542, 2022.
X. Li and D. Chen, “A survey on deep learning-based panoptic segmentation,” Digital Signal Processing: A Review Journal, vol. 120, p. 103283, 2022. [Online]. Available: https://doi.org/10.1016/j.dsp.2021.103283
A. Kirillov, K. He, R. Girshick, C. Rother, and P. Dollár, “Panoptic segmentation,” in 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2019, pp. 9396–9405.
R. P. Nikhil and K. P. Sankar, “A review on image segmentation techniques,” Pattern Recognition, vol. 26, no. 9, 1993.
M. Stewart. (2021, Feb.) Five big problems with labeled data. Zumo Labs. [Online]. Available: [link].
SAM. (2023) Segment anything. Acesso em 30/08/2023. [Online]. Available: [link]
M. A. Mazurowski, H. Dong, H. Gu, J. Yang, N. Konz, and Y. Zhang, “Segment anything model for medical image analysis: An experimental study,” Medical Image Analysis, vol. 89, p. 102918, 2023. [Online]. Available: [link].
K. Sofiiuk, I. A. Petrov, and A. Konushin, “Reviving iterative training with mask guidance for interactive segmentation,” in 2022 IEEE International Conference on Image Processing (ICIP), 2022, pp. 3141–3145.
Q. Liu, Z. Xu, G. Bertasius, and M. Niethammer, “Simpleclick: Interactive image segmentation with simple vision transformers,” 2023.
X. Chen, Z. Zhao, Y. Zhang, M. Duan, D. Qi, and H. Zhao, “Focalclick: Towards practical interactive image segmentation,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), June 2022, pp. 1300–1309.
S. Mohapatra, A. Gosai, and G. Schlaug, “SAM vs BET: A comparative study for brain extraction and segmentation of magnetic resonance images using deep learning,” Apr. 2023, v1, last revised 19 Apr 2023 (this version, v3). [Online]. Available: [link]
S. M. Smith, “Fast robust automated brain extraction,” Human Brain Mapping, vol. 17, no. 3, pp. 143–155, 2002. [Online]. Available: [link]
I. Giannakis, A. Bhardwaj, L. Sam, and G. Leontidis, “Deep learning universal crater detection using segment anything model (SAM),” Cornell University, Apr. 2023. [Online]. Available: [link]
A. Kirillov, E. Mintun, N. Ravi, H. Mao, C. Rolland, L. Gustafson, T. Xiao, S. Whitehead, A. C. Berg, W.-Y. Lo, P. Dollár, and R. Girshick, “Segment anything,” 2023.
D. Jha, P. H. Smedsrud, M. A. Riegler, P. Halvorsen, T. de Lange, D. Johansen, and H. D. Johansen, “Kvasir-seg: A segmented polyp dataset,” in MultiMedia Modeling, Y. M. Ro, W.-H. Cheng, J. Kim, W.-T. Chu, P. Cui, J.-W. Choi, M.-C. Hu, and W. De Neve, Eds. Cham: Springer International Publishing, 2020, pp. 451–462.
P. Rawla, T. Sunkara, and A. Barsouk, “Epidemiology of colorectal cancer: incidence, mortality, survival, and risk factors,” Gastroenterology Review/Przeglad Gastroenterologiczny, vol. 14, no. 2, pp. 89–103, 2019. [Online]. Available: http://dx.doi.org/10.5114/pg.2018.81072
H. Sung, J. Ferlay, R. L. Siegel, M. Laversanne, I. Soerjomataram, A. Jemal, and F. Bray, “Global cancer statistics 2020: Globocan estimates of incidence and mortality worldwide for 36 cancers in 185 countries,” CA: A Cancer Journal for Clinicians, vol. 71, no. 3, pp. 209–249, 2021. [Online]. Available: [link].
J. Andrade, A. Cordeiro, J. A. Junior, and P. P. Filho, “Reconhecimento de alfaces em sistema hidropônico utilizando técnicas de processamento digital de imagens,” in Anais do XIX Congresso Latino-Americano de Software Livre e Tecnologias Abertas. Porto Alegre, RS, Brasil: SBC, 2022, pp. 137–140. [Online]. Available: [link].
C. R. Brzezinski, J. Abati, A. Geller, F. Werner, and C. Zucareli, “Production of iceberg lettuce cultivars under two cropping systems,” Revista Ceres, vol. 64, pp. 83–89, 2017.
F. B. A. D. MEDEIROS, “Produção e qualidade de cultivares de alface americana em função do espaçamento de plantio,” 2015.
T.-Y. Lin, M. Maire, S. Belongie, J. Hays, P. Perona, D. Ramanan, P. Dollár, and C. L. Zitnick, “Microsoft coco: Common objects in context,” in Computer Vision – ECCV 2014, D. Fleet, T. Pajdla, B. Schiele, and T. Tuytelaars, Eds. Cham: Springer International Publishing, 2014, pp. 740–755.
S. S. A. Zaidi, M. S. Ansari, A. Aslam, N. Kanwal, M. Asghar, and B. Lee, “A survey of modern deep learning based object detection models,” Digital Signal Processing, vol. 126, p. 103514, 2022. [Online]. Available: [link].
X. Liu, L. Song, S. Liu, and Y. Zhang, “A review of deep-learning-based medical image segmentation methods,” Sustainability, vol. 13, no. 3, 2021. [Online]. Available: [link]
R. C. Gonzalez and R. E. Woods, “Processamento digital de imagens,” vol. 3 ed., 2009.
M. F. S. Saldanha and C. C. Freitas, “Segmentação de imagens digitais: Uma revisão,” in Anais do 9º Workshop dos Cursos de Computação Aplicada do INPE. São José dos Campos: INPE, 2009. [Online]. Available: [link]
S. Rajalingappaa, Deep Learning for Computer Vision. Birmingham, UK: Packt Publishing, Jan. 2018.
S. Minaee, Y. Boykov, F. Porikli, A. Plaza, N. Kehtarnavaz, and D. Terzopoulos, “Image segmentation using deep learning: A survey,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 44, no. 7, pp. 3523–3542, 2022.
X. Li and D. Chen, “A survey on deep learning-based panoptic segmentation,” Digital Signal Processing: A Review Journal, vol. 120, p. 103283, 2022. [Online]. Available: https://doi.org/10.1016/j.dsp.2021.103283
A. Kirillov, K. He, R. Girshick, C. Rother, and P. Dollár, “Panoptic segmentation,” in 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2019, pp. 9396–9405.
R. P. Nikhil and K. P. Sankar, “A review on image segmentation techniques,” Pattern Recognition, vol. 26, no. 9, 1993.
M. Stewart. (2021, Feb.) Five big problems with labeled data. Zumo Labs. [Online]. Available: [link].
SAM. (2023) Segment anything. Acesso em 30/08/2023. [Online]. Available: [link]
M. A. Mazurowski, H. Dong, H. Gu, J. Yang, N. Konz, and Y. Zhang, “Segment anything model for medical image analysis: An experimental study,” Medical Image Analysis, vol. 89, p. 102918, 2023. [Online]. Available: [link].
K. Sofiiuk, I. A. Petrov, and A. Konushin, “Reviving iterative training with mask guidance for interactive segmentation,” in 2022 IEEE International Conference on Image Processing (ICIP), 2022, pp. 3141–3145.
Q. Liu, Z. Xu, G. Bertasius, and M. Niethammer, “Simpleclick: Interactive image segmentation with simple vision transformers,” 2023.
X. Chen, Z. Zhao, Y. Zhang, M. Duan, D. Qi, and H. Zhao, “Focalclick: Towards practical interactive image segmentation,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), June 2022, pp. 1300–1309.
S. Mohapatra, A. Gosai, and G. Schlaug, “SAM vs BET: A comparative study for brain extraction and segmentation of magnetic resonance images using deep learning,” Apr. 2023, v1, last revised 19 Apr 2023 (this version, v3). [Online]. Available: [link]
S. M. Smith, “Fast robust automated brain extraction,” Human Brain Mapping, vol. 17, no. 3, pp. 143–155, 2002. [Online]. Available: [link]
I. Giannakis, A. Bhardwaj, L. Sam, and G. Leontidis, “Deep learning universal crater detection using segment anything model (SAM),” Cornell University, Apr. 2023. [Online]. Available: [link]
A. Kirillov, E. Mintun, N. Ravi, H. Mao, C. Rolland, L. Gustafson, T. Xiao, S. Whitehead, A. C. Berg, W.-Y. Lo, P. Dollár, and R. Girshick, “Segment anything,” 2023.
D. Jha, P. H. Smedsrud, M. A. Riegler, P. Halvorsen, T. de Lange, D. Johansen, and H. D. Johansen, “Kvasir-seg: A segmented polyp dataset,” in MultiMedia Modeling, Y. M. Ro, W.-H. Cheng, J. Kim, W.-T. Chu, P. Cui, J.-W. Choi, M.-C. Hu, and W. De Neve, Eds. Cham: Springer International Publishing, 2020, pp. 451–462.
P. Rawla, T. Sunkara, and A. Barsouk, “Epidemiology of colorectal cancer: incidence, mortality, survival, and risk factors,” Gastroenterology Review/Przeglad Gastroenterologiczny, vol. 14, no. 2, pp. 89–103, 2019. [Online]. Available: http://dx.doi.org/10.5114/pg.2018.81072
H. Sung, J. Ferlay, R. L. Siegel, M. Laversanne, I. Soerjomataram, A. Jemal, and F. Bray, “Global cancer statistics 2020: Globocan estimates of incidence and mortality worldwide for 36 cancers in 185 countries,” CA: A Cancer Journal for Clinicians, vol. 71, no. 3, pp. 209–249, 2021. [Online]. Available: [link].
J. Andrade, A. Cordeiro, J. A. Junior, and P. P. Filho, “Reconhecimento de alfaces em sistema hidropônico utilizando técnicas de processamento digital de imagens,” in Anais do XIX Congresso Latino-Americano de Software Livre e Tecnologias Abertas. Porto Alegre, RS, Brasil: SBC, 2022, pp. 137–140. [Online]. Available: [link].
C. R. Brzezinski, J. Abati, A. Geller, F. Werner, and C. Zucareli, “Production of iceberg lettuce cultivars under two cropping systems,” Revista Ceres, vol. 64, pp. 83–89, 2017.
F. B. A. D. MEDEIROS, “Produção e qualidade de cultivares de alface americana em função do espaçamento de plantio,” 2015.
T.-Y. Lin, M. Maire, S. Belongie, J. Hays, P. Perona, D. Ramanan, P. Dollár, and C. L. Zitnick, “Microsoft coco: Common objects in context,” in Computer Vision – ECCV 2014, D. Fleet, T. Pajdla, B. Schiele, and T. Tuytelaars, Eds. Cham: Springer International Publishing, 2014, pp. 740–755.
S. S. A. Zaidi, M. S. Ansari, A. Aslam, N. Kanwal, M. Asghar, and B. Lee, “A survey of modern deep learning based object detection models,” Digital Signal Processing, vol. 126, p. 103514, 2022. [Online]. Available: [link].
X. Liu, L. Song, S. Liu, and Y. Zhang, “A review of deep-learning-based medical image segmentation methods,” Sustainability, vol. 13, no. 3, 2021. [Online]. Available: [link]
Publicado
18/10/2023
Como Citar
ARAUJO JUNIOR, Sandro Luis de; ANDRADE, Jakeline da Silva; PAULA FILHO, Pedro Luiz de; FRANCO, Ricardo.
Ferramenta para auxílio na rotulação de datasets para segmentação semântica. In: CONGRESSO LATINO-AMERICANO DE SOFTWARE LIVRE E TECNOLOGIAS ABERTAS (LATINOWARE), 20. , 2023, Foz do Iguaçu/PR.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2023
.
p. 15-21.
DOI: https://doi.org/10.5753/latinoware.2023.236286.
