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|Título:||Segmentation of kidney and renal collecting system on 3D computed tomography images|
Torres, Helena Daniela Ribeiro
Queirós, Sandro Filipe Monteiro
Fonseca, Jaime C.
Rodrigues, Nuno F.
Vilaca, João L.
coupled B-Spline Explicit Active Surfaces
patient-specific virtual environment
renal collecting system segmentation
|Citação:||Oliveira, B., Torres, H. R., Queirós, S., Morais, P., Fonseca, J. C., D'hooge, J., ... & Vilaça, J. L. (2018, May). Segmentation of kidney and renal collecting system on 3D computed tomography images. In 2018 IEEE 6th International Conference on Serious Games and Applications for Health (SeGAH) (pp. 1-8). IEEE|
|Resumo(s):||Surgical training for minimal invasive kidney interventions (MIKI) has huge importance within the urology field. Within this topic, simulate MIKI in a patient-specific virtual environment can be used for pre-operative planning using the real patient's anatomy, possibly resulting in a reduction of intra-operative medical complications. However, the validated VR simulators perform the training in a group of standard models and do not allow patient-specific training. For a patient-specific training, the standard simulator would need to be adapted using personalized models, which can be extracted from pre-operative images using segmentation strategies. To date, several methods have already been proposed to accurately segment the kidney in computed tomography (CT) images. However, most of these works focused on kidney segmentation only, neglecting the extraction of its internal compartments. In this work, we propose to adapt a coupled formulation of the B-Spline Explicit Active Surfaces (BEAS) framework to simultaneously segment the kidney and the renal collecting system (CS) from CT images. Moreover, from the difference of both kidney and CS segmentations, one is able to extract the renal parenchyma also. The segmentation process is guided by a new energy functional that combines both gradient and region-based energies. The method was evaluated in 10 kidneys from 5 CT datasets, with different image properties. Overall, the results demonstrate the accuracy of the proposed strategy, with a Dice overlap of 92.5%, 86.9% and 63.5%, and a point-to-surface error around 1.6 mm, 1.9 mm and 4 mm for the kidney, renal parenchyma and CS, respectively.|
|Versão da editora:||https://ieeexplore.ieee.org/abstract/document/8401384|
|Aparece nas coleções:||ICVS - Artigos em Revistas Internacionais com Referee|
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|oliveira2018_vilaça2.pdf||571,78 kB||Adobe PDF||Ver/Abrir|