Semantic and structural image segmentation for prosthetic vision
Open accessAbstract
Prosthetic vision is being applied to partially recover the retinal stimulation of visually impaired people. However, the phosphenic images produced by the implants have very limited information bandwidth due to the poor resolution and lack of color or contrast. The ability of object recognition and scene understanding in real environments is severely restricted for prosthetic users. Computer vision can play a key role to overcome the limitations and to optimize the visual information in the prosthetic vision, improving the amount of information that is presented. We present a new approach to build a schematic representation of indoor environments for simulated phosphene images. The proposed method combines a variety of convolutional neural networks for extracting and conveying relevant information about the scene such as structural informative edges of the environment and silhouettes of segmented objects. Experiments were conducted with normal sighted subjects with a Simulated Prosthetic Vision system. The results show good accuracy for object recognition and room identification tasks for indoor scenes using the proposed approach, compared to other image processing methods.
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The use of pre-trained convolutional neural networks was proposed in three studies as a pre-processing step for saliency extraction.
“The use of pre-trained convolutional neural networks was proposed in three studies82–84 as a pre-processing step for saliency extraction.”
From Visual Prostheses in the Era of Artificial Intelligence Technology (2025) · cites this paper as [82]
The Methods > Stimuli section (page unavailable) describes using pre-trained convolutional models to extract informative object masks and structural edges before generating prosthetic-vision stimuli. It calls this semantic and structural segmentation, not saliency extraction, so that characterization is not fully supported. This paper also cannot establish the citing sentence’s count of three studies. (LLM, conf=0.79)
Experimental — accuracy not yet measuredThe models had been pre-trained on natural image datasets.
“The models had been pre-trained on natural image datasets.”
From Visual Prostheses in the Era of Artificial Intelligence Technology (2025) · cites this paper as [82]
These three studies proposed to combine outputs of two deep learning models to build their saliency maps.
“These three studies have proposed to combine outputs of two deep learning models to build their saliency maps (saliency with depth,83 contours with surface normals,84 contours with semantic masks).82”
From Visual Prostheses in the Era of Artificial Intelligence Technology (2025) · cites this paper as [82]
Relevant features include object and structural edge recognition in indoor rooms or clip-art style images conveying simplified representations of objects.
“Relevant features include object and structural edge recognition in indoor rooms82 or clip-art style images conveying simplified representations of objects.86”
From Visual Prostheses in the Era of Artificial Intelligence Technology (2025) · cites this paper as [82]
Preprocessing with semantic segmentation may successfully improve object recognition performance in simulated prosthetic vision compared to conventional edge detection techniques.
“Note that a variation on these approaches is investigated in Sanchez-Garcia et al. (2020), who demonstrated that preprocessing with semantic segmentation may successfully improve object recognition performance in simulated prosthetic vision (compared to preprocessing with conventional edge detection techniques).”
From End-to-end optimization of prosthetic vision (2022) · cites this paper as Sanchez-Garcia et al. (2020)
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Cited by
Sorted by citations
Neurolight: A Deep Learning Neural Interface for Cortical Visual Prostheses
Lozano, A., Suárez, J. S., Soto-Sánchez, C., Garrigós, J., Martínez-Alvarez, J. J., Ferrández, J. M., & Fernández, E. (2020) · International Journal of Neural Systems · doi:10.1142/s0129065720500458 · Cited by 57 · see citing works
End-to-end optimization of prosthetic vision
de Ruyter van Steveninck, J., Güçlü, U., van Wezel, R., & van Gerven, M. (2022) · Journal of Vision · doi:10.1167/jov.22.2.20 · Cited by 54 · see citing works
de Ruyter van Steveninck, J., van Gestel, T., Koenders, P., van der Ham, G., Vereecken, F., Güçlü, U., van Gerven, M., Güçlütürk, Y., & van Wezel, R. (2022) · Journal of Vision · doi:10.1167/jov.22.2.1 · Cited by 51 · see citing works
Fernández, E., Alfaro, A., & González-López, P. (2020) · Frontiers in Neuroscience · doi:10.3389/fnins.2020.00681 · Cited by 35 · see citing works
van der Grinten, M., de Ruyter van Steveninck, J., Lozano, A., Pijnacker, L., Rueckauer, B., Roelfsema, P., van Gerven, M., van Wezel, R., Güçlü, U., & Güçlütürk, Y. (2024) · eLife · doi:10.7554/elife.85812 · Cited by 39 · see citing works
Beyeler, M., & Sanchez-Garcia, M. (2022) · Journal of Neural Engineering · doi:10.1088/1741-2552/aca69d · Cited by 34 · see citing works
Self-citationOptimization of Neuroprosthetic Vision via End-to-end Deep Reinforcement Learning
Küçükoğlu, B., Rueckauer, B., Ahmad, N., de Ruyter van Steveninck, J., Güçlü, U., & van Gerven, M. (2022) · doi:10.1101/2022.02.25.482017 · Cited by 25 · see citing works
Advances in neuroscience, not devices, will determine the effectiveness of visual prostheses
Abbasi, B., & Rizzo, J. F. (2021) · Seminars in Ophthalmology · doi:10.1080/08820538.2021.1887902 · Cited by 19 · see citing works
An actor-model framework for visual sensory encoding
Leong, F., Rahmani, B., Psaltis, D., Moser, C., & Ghezzi, D. (2024) · Nature Communications · doi:10.1038/s41467-024-45105-5 · Cited by 8 · see citing works
End-to-end optimization of prosthetic vision
de Ruyter van Steveninck, J., Güçlü, U., van Wezel, R., & van Gerven, M. (2020) · doi:10.1101/2020.12.19.423601 · Cited by 54 · see citing works
“Note that a variation on these approaches is investigated in Sanchez-Garcia et al. (2020), who demonstrated that preprocessing with semantic segmentation may successfully improve object recognition performance in simulated prosthetic vision (compared to preprocessing with conventional edge…”
BackgroundMethodResultvan der Grinten, M., de Ruyter van Steveninck, J., Lozano, A., Pijnacker, L., Rückauer, B., Roelfsema, P., van Gerven, M., van Wezel, R., Güçlü, U., & Güçlütürk, Y. (2022) · doi:10.1101/2022.12.23.521749 · Cited by 39 · see citing works
Visual Prostheses in the Era of Artificial Intelligence Technology
Sarbout, I., Gungor, A., Ounissi, M., Zaher, S., Ptito, M., Kupers, R., Racoceanu, D., & Milea, D. (2025) · Eye and Brain · doi:10.2147/eb.s524322 · Cited by 3 · see citing works
“Other features included contrast information, 73,76 semantic information, 80,82,83 depth information, 76,79,81 surface normal, 84 convolutional filters, 73,75,77 and the use of clip-art style images.”
Influential
- Authors
- Sanchez-Garcia, M., Martinez-Cantin, R., & Guerrero, J. J.
- Affiliations
- Universidad de Zaragoza (ES)
- Corresponding author
- Sanchez-Garcia, Melani
- Venue
- PLOS ONE
- Publisher
- Public Library of Science (PLoS)
- Published
- 2020-01-29
- Volume
- 15
- Issue
- 1
- Pages
- e0227677
- ISSN
- 1932-6203 (ISSN-L)
- PMC
- PMC6988941
- OpenAlex
- W3003581481
- Cited by
- 62
- References
- 97
- Citations by year
- 2026: 7 · 2025: 14 · 2024: 6 · 2023: 8 · 2022: 16 · 2021: 6 · 2020: 4 · 2019: 1
- FWCI
- 3.28
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- PLoS ONE (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- Zaguan (University of Zaragoza Repository) (submittedVersion, cc-by)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- arXiv (Cornell University)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Life Sciences › Neuroscience › Cellular and Molecular Neuroscience › Neuroscience and Neural Engineering
- Life Sciences › Neuroscience › Cognitive Neuroscience › Tactile and Sensory Interactions
- Keywords
- prosthetic vision, visual prosthesis, image segmentation, semantic segmentation, structural segmentation, convolutional neural networks, object recognition, room identification, indoor scenes, visually impaired people, retinal prosthesis
- MeSH terms
- Adult, Artificial Intelligence, Computer Simulation, Female, Humans, Image Processing, Computer-Assisted, Male, Middle Aged, Phosphenes, Photic Stimulation, Psychophysics, Semantics, Vision Disorders, Visual Perception, Young Adult, Visual Prosthesis, Healthy Volunteers
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