Reconstructing Nerve Structures from Unorganized Points
Realistic sensory feedback is paramount for amputees as it improves prosthetic limb control and boosts functionality, safety, and overall quality of life. This sensory restoration relies on the direct electrostimulation of residual peripheral nerves. Computational models are instrumental in simulati...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-10-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/13/20/11421 |
| _version_ | 1797574922431627264 |
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| author | Jelena Kljajić Goran Kvaščev Željko Đurović |
| author_facet | Jelena Kljajić Goran Kvaščev Željko Đurović |
| author_sort | Jelena Kljajić |
| collection | DOAJ |
| description | Realistic sensory feedback is paramount for amputees as it improves prosthetic limb control and boosts functionality, safety, and overall quality of life. This sensory restoration relies on the direct electrostimulation of residual peripheral nerves. Computational models are instrumental in simulating these neurostimulation effects, offering solutions to the complexities tied to extensive animal/human trials and costly materials. Central to these models is the detailed mapping of nerve geometry, necessitating the delineation of internal nerve structures, such as fascicles, across various cross-sections. In our modeling process, we faced the challenge of organizing an originally unstructured set of points into coherent contours. We introduced a parameter-free curve-reconstruction algorithm that combines valley-seeking clustering, an adaptive Kalman filter, and the nearest neighbor classification technique. While intuitively simple for humans, the task of reconstructing multiple open and/or closed lines with pronounced corners from a nonuniform point set is daunting for many algorithms. Additionally, the precise differentiation of adjacent curves, commonly encountered in realistic nerve models, remains a formidable challenge even for top-tier algorithms. Our proposed method adeptly navigates the complexities inherent to nerve structure reconstruction. While our algorithm is chiefly designed for closed curves, as dictated by nerve geometry, we believe it can be reconfigured with appropriate code adjustments to handle open curves. Beyond neuroprosthetics, our proposed model has the potential to be applied and spark innovations in biomedicine and a variety of other fields. |
| first_indexed | 2024-03-10T21:28:57Z |
| format | Article |
| id | doaj.art-fade2c974d5f44e4ac467a7df5efaa36 |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-10T21:28:57Z |
| publishDate | 2023-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-fade2c974d5f44e4ac467a7df5efaa362023-11-19T15:31:58ZengMDPI AGApplied Sciences2076-34172023-10-0113201142110.3390/app132011421Reconstructing Nerve Structures from Unorganized PointsJelena Kljajić0Goran Kvaščev1Željko Đurović2School of Electrical Engineering, University of Belgrade, 11000 Belgrade, SerbiaSchool of Electrical Engineering, University of Belgrade, 11000 Belgrade, SerbiaSchool of Electrical Engineering, University of Belgrade, 11000 Belgrade, SerbiaRealistic sensory feedback is paramount for amputees as it improves prosthetic limb control and boosts functionality, safety, and overall quality of life. This sensory restoration relies on the direct electrostimulation of residual peripheral nerves. Computational models are instrumental in simulating these neurostimulation effects, offering solutions to the complexities tied to extensive animal/human trials and costly materials. Central to these models is the detailed mapping of nerve geometry, necessitating the delineation of internal nerve structures, such as fascicles, across various cross-sections. In our modeling process, we faced the challenge of organizing an originally unstructured set of points into coherent contours. We introduced a parameter-free curve-reconstruction algorithm that combines valley-seeking clustering, an adaptive Kalman filter, and the nearest neighbor classification technique. While intuitively simple for humans, the task of reconstructing multiple open and/or closed lines with pronounced corners from a nonuniform point set is daunting for many algorithms. Additionally, the precise differentiation of adjacent curves, commonly encountered in realistic nerve models, remains a formidable challenge even for top-tier algorithms. Our proposed method adeptly navigates the complexities inherent to nerve structure reconstruction. While our algorithm is chiefly designed for closed curves, as dictated by nerve geometry, we believe it can be reconfigured with appropriate code adjustments to handle open curves. Beyond neuroprosthetics, our proposed model has the potential to be applied and spark innovations in biomedicine and a variety of other fields.https://www.mdpi.com/2076-3417/13/20/11421nerve geometrycurve reconstructionunorganized pointsmultiple curvesneuroprostheticsneurostimulation |
| spellingShingle | Jelena Kljajić Goran Kvaščev Željko Đurović Reconstructing Nerve Structures from Unorganized Points Applied Sciences nerve geometry curve reconstruction unorganized points multiple curves neuroprosthetics neurostimulation |
| title | Reconstructing Nerve Structures from Unorganized Points |
| title_full | Reconstructing Nerve Structures from Unorganized Points |
| title_fullStr | Reconstructing Nerve Structures from Unorganized Points |
| title_full_unstemmed | Reconstructing Nerve Structures from Unorganized Points |
| title_short | Reconstructing Nerve Structures from Unorganized Points |
| title_sort | reconstructing nerve structures from unorganized points |
| topic | nerve geometry curve reconstruction unorganized points multiple curves neuroprosthetics neurostimulation |
| url | https://www.mdpi.com/2076-3417/13/20/11421 |
| work_keys_str_mv | AT jelenakljajic reconstructingnervestructuresfromunorganizedpoints AT gorankvascev reconstructingnervestructuresfromunorganizedpoints AT zeljkođurovic reconstructingnervestructuresfromunorganizedpoints |