Segmentation of kidney and renal collecting system on 3D computed tomography images
| Autor: | Oliveira, Bruno, Torres, Helena Daniela Ribeiro, Queirós, Sandro Filipe Monteiro, Morais, Pedro, Fonseca, Jaime C., D'hooge, Jan, Rodrigues, Nuno F., Vilaca, João L. |
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| Přispěvatelé: | Universidade do Minho |
| Jazyk: | angličtina |
| Rok vydání: | 2018 |
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| Popis: | 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. NORTE-01-0145-FEDER0000I3, and NORTE-01-0145-FEDER-024300, supported by Northern Portugal Regional Operational Programme (Norte2020), under the Portugal 2020 Partnership Agreement, through the European Regional Development Fund (FEDER), and also been funded by FEDER funds, through Competitiveness Factors Operational Programme (COMPETE), and by national funds, through the FCT-Fundacao para a Ciência e Tecnologia, under the scope of the project POCI-01-0145-FEDER-007038. The authors acknowledge FCT-Fundação para a Ciância e a Tecnologia, Portugal, and the European Social Found, European Union, for funding support through the Programa Operacional Capital Humano (POCH). info:eu-repo/semantics/publishedVersion |
| Databáze: | OpenAIRE |
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