Mr. Dilakshan Srikanthan Profile

Dilakshan Srikanthan

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Publications (3)

Proceedings Article | 3 April 2024 Presentation + Paper

Using NURBS for virtual resections in liver surgery planning: a comparative usability study

Gabriella d'Albenzio, Rebecca Hisey, Dilakshan Srikanthan, Tamas Ungi, Andras Lasso, Davit Aghayan, Gabor Fichtinger, Rafael Palomar

Proceedings Volume 12927, 129270Z (2024) https://doi.org/10.1117/12.3006486

KEYWORDS: Resection, Liver, Surgery, Deformation, 3D modeling, Medical imaging, Liver cancer

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PURPOSE: Accurate preoperative planning is crucial for liver resection surgery due to the complex anatomical structures and variations among patients. The need of virtual resections utilizing deformable surfaces presents a promising approach for effective liver surgery planning. However, the range of available surface definitions poses the question of which definition is most appropriate. METHODS: The study compares the use of NURBS and B´ezier surfaces for the definition of virtual resections through a usability study, where 25 participants (19 biomedical researchers and 6 liver surgeons) completed tasks using varying numbers of control points driving surface deformations and different surface types. Specifically, participants aim to perform virtual liver resections using 16 and 9 control points for NURBS and B´ezier surfaces. The goal is to assess whether they can attain an optimal resection plan, effectively balancing complete tumor removal with the preservation of enough healthy liver tissue and function to prevent postoperative liver dysfunction, despite working with fewer control points and different surface properties. Accuracy was assessed using Hausdorff distance and average surface distance. A survey based on the NASA Task Load Index measured user performance and preferences. RESULTS: NURBS surfaces exhibit improved accuracy and consistency over B´ezier surfaces, with lower average surface distance and variability of results. The 95th percentile Hausdorff Distance indicates the robustness of NURBS surfaces for the task. Task completion time was influenced by control point dimensions, favoring NURBS 3x3 (vs. 4x4) surfaces for a balanced accuracy-efficiency trade-off. Finally, the survey results indicated participants preferred NURBS surfaces over B´ezier, emphasizing the improved performance, surface manipulation, and reduced effort. CONCLUSION: The integration of NURBS surfaces into liver resection planning offers a promising advancement. This study demonstrates their superiority in accuracy, efficiency, and user preference compared to B´ezier surfaces. The findings underscore the potential of NURBS-based preoperative planning tools to enhance surgical outcomes in liver resection procedures.

Proceedings Article | 3 April 2023 Presentation + Paper

Attention-based multi-instance learning for improved glioblastoma detection using mass spectrometry

Dilakshan Srikanthan, Martin Kaufmann, Amoon Jamzad, Ayesha Syeda, Alice Santilli, Alireza Sedghi, Gabor Fichtinger, Jamie Purzner, John Rudan, Teresa Purzner, Parvin Mousavi

Proceedings Volume 12466, 1246611 (2023) https://doi.org/10.1117/12.2654436

KEYWORDS: Surgery, Tumors, Tissues, Brain, Education and training, Resection, Brain tissue, Cancer detection, Nervous system, Mass spectrometry

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Proceedings Article | 3 April 2023 Presentation + Paper

Self-supervised learning and uncertainty estimation for surgical margin detection

Ayesha Syeda, Fahimeh Fooladgar, Amoon Jamzad, Dilakshan Srikanthan, Martin Kaufmann, Kevin Ren, Jay Engel, Ross Walker, Shaila Merchant, Doug McKay, Sonal Varma, Gabor Fichtinger, John Rudan, Parvin Mousavi

Proceedings Volume 12466, 124660B (2023) https://doi.org/10.1117/12.2654104

KEYWORDS: Data modeling, Tumor growth modeling, Education and training, Tissues, Cancer, Surgery, Machine learning, Breast cancer, Tumors, Performance modeling

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