Texture-Based Preprocessing Framework with nnU-Net Model for Accurate Intracranial Artery Segmentation
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| Publikašuvnnas: | Journal of Imaging vol. 11, no. 12 (2025), p. 438-459 |
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| Váldodahkki: | |
| Eará dahkkit: | |
| Almmustuhtton: |
MDPI AG
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| Fáttát: | |
| Liŋkkat: | Citation/Abstract Full Text + Graphics Full Text - PDF |
| Fáddágilkorat: |
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|---|---|---|---|
| 001 | 3286310140 | ||
| 003 | UK-CbPIL | ||
| 022 | |a 2313-433X | ||
| 024 | 7 | |a 10.3390/jimaging11120438 |2 doi | |
| 035 | |a 3286310140 | ||
| 045 | 2 | |b d20250101 |b d20251231 | |
| 100 | 1 | |a Kyuseok, Kim |u Institute of Human Convergence Health Science, Gachon University, 191, Hambakmoero, Yeonsu-gu, Incheon 21936, Republic of Korea; kskim502@gachon.ac.kr | |
| 245 | 1 | |a Texture-Based Preprocessing Framework with nnU-Net Model for Accurate Intracranial Artery Segmentation | |
| 260 | |b MDPI AG |c 2025 | ||
| 513 | |a Journal Article | ||
| 520 | 3 | |a Accurate intracranial artery segmentation from digital subtraction angiography (DSA) is critical for neurovascular diagnosis and intervention planning. Vascular extraction, which combines preprocessing methods and deep learning models, yields a high level of results, but limited preprocessing results constrain the improvement of results. We propose a texture-based contrast enhancement preprocessing framework integrated with the nnU-Net model to improve vessel segmentation in time-sequential DSA images. The method generates a combined feature mask by fusing local contrast, local entropy, and brightness threshold maps, which is then used as input for deep learning–based segmentation. Segmentation performance was evaluated using the DIAS dataset with various standard quantitative metrics. The proposed preprocessing significantly improved segmentation across all metrics compared to both the baseline and contrast-limited adaptive histogram equalization (CLAHE). Using nnU-Net, the method achieved a Dice Similarity Coefficient (DICE) of 0.83 ± 0.20 and an Intersection over Union (IoU) of 0.72 ± 0.14, outperforming CLAHE (DICE 0.79 ± 0.41, IoU 0.70 ± 0.23) and the baseline (DICE 0.65 ± 0.15, IoU 0.47 ± 0.20). Most notably, vessel connectivity (VC) dropped by over 65% relative to unprocessed images, indicating marked improvements in VC and topological accuracy. This study demonstrates that combining texture-based preprocessing with nnU-Net delivers robust, noise-tolerant, and clinically interpretable segmentation of intracranial arteries from DSA. | |
| 653 | |a Accuracy | ||
| 653 | |a Preprocessing | ||
| 653 | |a Deep learning | ||
| 653 | |a Datasets | ||
| 653 | |a Blood vessels | ||
| 653 | |a Image segmentation | ||
| 653 | |a Neural networks | ||
| 653 | |a Veins & arteries | ||
| 653 | |a Algorithms | ||
| 653 | |a Time series | ||
| 653 | |a Medical imaging | ||
| 653 | |a Visualization | ||
| 653 | |a Entropy | ||
| 653 | |a Texture | ||
| 700 | 1 | |a Ji-Youn, Kim |u Department of Dental Hygiene, Gachon University, 191, Hambakmoero, Yeonsu-gu, Incheon 21936, Republic of Korea | |
| 773 | 0 | |t Journal of Imaging |g vol. 11, no. 12 (2025), p. 438-459 | |
| 786 | 0 | |d ProQuest |t Advanced Technologies & Aerospace Database | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/3286310140/abstract/embedded/L8HZQI7Z43R0LA5T?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text + Graphics |u https://www.proquest.com/docview/3286310140/fulltextwithgraphics/embedded/L8HZQI7Z43R0LA5T?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text - PDF |u https://www.proquest.com/docview/3286310140/fulltextPDF/embedded/L8HZQI7Z43R0LA5T?source=fedsrch |