Utilize este identificador para referenciar este registo: http://hdl.handle.net/1822/58211

TítuloSegmentation of kidney and renal collecting system on 3D computed tomography images
Autor(es)Oliveira, Bruno
Torres, Helena Daniela Ribeiro
Queirós, Sandro Filipe Monteiro
Morais, Pedro
Fonseca, Jaime C.
D'hooge, Jan
Rodrigues, Nuno F.
Vilaca, João L.
Palavras-chaveComputed Tomography
coupled B-Spline Explicit Active Surfaces
kidney segmentation
patient-specific virtual environment
renal collecting system segmentation
Data2018
EditoraIEEE
CitaçãoOliveira, 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.
TipoconferencePaper
URIhttp://hdl.handle.net/1822/58211
ISBN978-1-5386-6299-1
e-ISBN978-1-5386-6298-4
DOI10.1109/SeGAH.2018.8401384
ISSN2573-3060
Versão da editorahttps://ieeexplore.ieee.org/abstract/document/8401384
Arbitragem científicayes
AcessoopenAccess
Aparece nas coleções:ICVS - Artigos em Revistas Internacionais com Referee

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