@InProceedings{8902794,
  author = {R. d. Amor and S. Morales and A. n. Colomer and J. M. Mossi and D. Woldbye and K. Klemp and M. Larsen and V. Naranjo},
  booktitle = {2019 27th European Signal Processing Conference (EUSIPCO)},
  title = {Towards Automatic Glaucoma Assessment: An Encoder-decoder CNN for Retinal Layer Segmentation in Rodent OCT images},
  year = {2019},
  pages = {1-5},
  abstract = {Optical coherence tomography (OCT) is an important imaging modality that is used frequently to monitor the state of retinal layers both in humans and animals. Automated OCT analysis in rodents is an important method to study the possible toxic effect of treatments before the test in humans. In this paper, an automatic method to detect the most significant retinal layers in rat OCT images is presented. This algorithm is based on an encoder-decoder fully convolutional network (FCN) architecture combined with a robust method of post-processing. After the validation, it was demonstrated that the proposed method outperforms the commercial Insight image segmentation software. We obtained results (averaged absolute distance error) in the test set for the training database of 2.52 ± 0.80 μm. In the predictions done by the method, in a different database (only used for testing), we also achieve the promising results of 4.45 ± 3.02 μm.},
  keywords = {biomedical optical imaging;convolutional neural nets;diseases;eye;image coding;image segmentation;medical image processing;optical tomography;automatic glaucoma assessment;encoder-decoder CNN;retinal layer segmentation;rodent OCT images;optical coherence tomography;imaging modality;automated OCT analysis;automatic method;rat OCT;encoder-decoder fully convolutional network architecture;robust method;image segmentation software;toxic effect;Image segmentation;Retina;Training;Rats;Rodents;Databases;Convolution;Optical coherence tomography;rodent OCT;layer segmentation;convolutional neural network;glaucoma assessment},
  doi = {10.23919/EUSIPCO.2019.8902794},
  issn = {2076-1465},
  month = {Sep.},
  url = {https://www.eurasip.org/proceedings/eusipco/eusipco2019/proceedings/papers/1570533863.pdf},
}

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M.","Woldbye, D.","Klemp, K.","Larsen, M.","Naranjo, V."],"bibdata":{"bibtype":"inproceedings","type":"inproceedings","author":[{"firstnames":["R."],"propositions":["d."],"lastnames":["Amor"],"suffixes":[]},{"firstnames":["S."],"propositions":[],"lastnames":["Morales"],"suffixes":[]},{"firstnames":["A."],"propositions":["n."],"lastnames":["Colomer"],"suffixes":[]},{"firstnames":["J.","M."],"propositions":[],"lastnames":["Mossi"],"suffixes":[]},{"firstnames":["D."],"propositions":[],"lastnames":["Woldbye"],"suffixes":[]},{"firstnames":["K."],"propositions":[],"lastnames":["Klemp"],"suffixes":[]},{"firstnames":["M."],"propositions":[],"lastnames":["Larsen"],"suffixes":[]},{"firstnames":["V."],"propositions":[],"lastnames":["Naranjo"],"suffixes":[]}],"booktitle":"2019 27th European Signal Processing Conference (EUSIPCO)","title":"Towards Automatic Glaucoma Assessment: An Encoder-decoder CNN for Retinal Layer Segmentation in Rodent OCT images","year":"2019","pages":"1-5","abstract":"Optical coherence tomography (OCT) is an important imaging modality that is used frequently to monitor the state of retinal layers both in humans and animals. Automated OCT analysis in rodents is an important method to study the possible toxic effect of treatments before the test in humans. In this paper, an automatic method to detect the most significant retinal layers in rat OCT images is presented. This algorithm is based on an encoder-decoder fully convolutional network (FCN) architecture combined with a robust method of post-processing. After the validation, it was demonstrated that the proposed method outperforms the commercial Insight image segmentation software. We obtained results (averaged absolute distance error) in the test set for the training database of 2.52 ± 0.80 μm. In the predictions done by the method, in a different database (only used for testing), we also achieve the promising results of 4.45 ± 3.02 μm.","keywords":"biomedical optical imaging;convolutional neural nets;diseases;eye;image coding;image segmentation;medical image processing;optical tomography;automatic glaucoma assessment;encoder-decoder CNN;retinal layer segmentation;rodent OCT images;optical coherence tomography;imaging modality;automated OCT analysis;automatic method;rat OCT;encoder-decoder fully convolutional network architecture;robust method;image segmentation software;toxic effect;Image segmentation;Retina;Training;Rats;Rodents;Databases;Convolution;Optical coherence tomography;rodent OCT;layer segmentation;convolutional neural network;glaucoma assessment","doi":"10.23919/EUSIPCO.2019.8902794","issn":"2076-1465","month":"Sep.","url":"https://www.eurasip.org/proceedings/eusipco/eusipco2019/proceedings/papers/1570533863.pdf","bibtex":"@InProceedings{8902794,\n author = {R. d. Amor and S. Morales and A. n. Colomer and J. M. Mossi and D. Woldbye and K. Klemp and M. Larsen and V. Naranjo},\n booktitle = {2019 27th European Signal Processing Conference (EUSIPCO)},\n title = {Towards Automatic Glaucoma Assessment: An Encoder-decoder CNN for Retinal Layer Segmentation in Rodent OCT images},\n year = {2019},\n pages = {1-5},\n abstract = {Optical coherence tomography (OCT) is an important imaging modality that is used frequently to monitor the state of retinal layers both in humans and animals. Automated OCT analysis in rodents is an important method to study the possible toxic effect of treatments before the test in humans. In this paper, an automatic method to detect the most significant retinal layers in rat OCT images is presented. This algorithm is based on an encoder-decoder fully convolutional network (FCN) architecture combined with a robust method of post-processing. After the validation, it was demonstrated that the proposed method outperforms the commercial Insight image segmentation software. We obtained results (averaged absolute distance error) in the test set for the training database of 2.52 ± 0.80 μm. In the predictions done by the method, in a different database (only used for testing), we also achieve the promising results of 4.45 ± 3.02 μm.},\n keywords = {biomedical optical imaging;convolutional neural nets;diseases;eye;image coding;image segmentation;medical image processing;optical tomography;automatic glaucoma assessment;encoder-decoder CNN;retinal layer segmentation;rodent OCT images;optical coherence tomography;imaging modality;automated OCT analysis;automatic method;rat OCT;encoder-decoder fully convolutional network architecture;robust method;image segmentation software;toxic effect;Image segmentation;Retina;Training;Rats;Rodents;Databases;Convolution;Optical coherence tomography;rodent OCT;layer segmentation;convolutional neural network;glaucoma assessment},\n doi = {10.23919/EUSIPCO.2019.8902794},\n issn = {2076-1465},\n month = {Sep.},\n url = {https://www.eurasip.org/proceedings/eusipco/eusipco2019/proceedings/papers/1570533863.pdf},\n}\n\n","author_short":["d. 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