Koyo Nakayama1, Atsushi Saito2, Elijah Biggs3, Marius George Linguraru3, Akinobu Shimizu2. 1. Tokyo University of Agriculture and Technology, 2-24-16 Naka-cho, Koganei, Tokyo, 184-8588, Japan. taiyou.003.084@gmail.com. 2. Tokyo University of Agriculture and Technology, 2-24-16 Naka-cho, Koganei, Tokyo, 184-8588, Japan. 3. Sheikh Zayed Institute for Pediatric Surgical Innovation, Children's National Health System, 111 Michigan Avenue, NW, Washington, DC, 20010, USA.
Abstract
PURPOSE: The pediatric computed tomography (CT) volume is acquired at a low dose because radiation is harmful to young children. Consequently, the pediatric CT volume has lower signal-to-noise ratio, which makes organ segmentation difficult. In this paper, we propose a liver segmentation algorithm for pediatric CT scan using a patient-specific level set distribution model (LSDM). METHODS: The patient-specific LSDM was constructed using a conditional LSDM (C-LSDM) conditioned on age. Furthermore, a patient-specific probabilistic atlas (PA) was generated using the model, which became a priori to the maximum a posteriori-based segmentation. The patient-specific PA generation by the C-LSDM using kernel density estimation was quicker than the conventional PA generation method using random numbers, and also, it was more accurate as it did not include any approximations. RESULTS: The liver segmentation algorithm was tested on 42 CT volumes of children aged between 2 weeks and 7 years. In the proposed method, the calculation time of the PA was about 9 s for the single Gaussian method, while it was 337 s for the conventional PA generation method using random numbers. Furthermore, using the kernel density estimation, median and 25%/75% tile of the generalized Dice similarity index between the PA and the correct liver region were found to be 0.3443 and 0.3191/0.3595. The Dice similarity index in the segmentation was 0.8821 and 0.8545/0.9085, which are higher than those obtained by the conventional method, and requires lower computational cost. CONCLUSION: We proposed a method to quickly and accurately generate a PA, combined with C-LSDM using kernel density estimation, which enabled efficient calculation and improved segmentation accuracy.
PURPOSE: The pediatric computed tomography (CT) volume is acquired at a low dose because radiation is harmful to young children. Consequently, the pediatric CT volume has lower signal-to-noise ratio, which makes organ segmentation difficult. In this paper, we propose a liver segmentation algorithm for pediatric CT scan using a patient-specific level set distribution model (LSDM). METHODS: The patient-specific LSDM was constructed using a conditional LSDM (C-LSDM) conditioned on age. Furthermore, a patient-specific probabilistic atlas (PA) was generated using the model, which became a priori to the maximum a posteriori-based segmentation. The patient-specific PA generation by the C-LSDM using kernel density estimation was quicker than the conventional PA generation method using random numbers, and also, it was more accurate as it did not include any approximations. RESULTS: The liver segmentation algorithm was tested on 42 CT volumes of children aged between 2 weeks and 7 years. In the proposed method, the calculation time of the PA was about 9 s for the single Gaussian method, while it was 337 s for the conventional PA generation method using random numbers. Furthermore, using the kernel density estimation, median and 25%/75% tile of the generalized Dice similarity index between the PA and the correct liver region were found to be 0.3443 and 0.3191/0.3595. The Dice similarity index in the segmentation was 0.8821 and 0.8545/0.9085, which are higher than those obtained by the conventional method, and requires lower computational cost. CONCLUSION: We proposed a method to quickly and accurately generate a PA, combined with C-LSDM using kernel density estimation, which enabled efficient calculation and improved segmentation accuracy.
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