TY - GEN
T1 - Segmentation of pelvic vessels in pediatric MRI using a patch-based deep learning approach
AU - Virzì, A.
AU - Gori, P.
AU - Muller, C. O.
AU - Mille, E.
AU - Peyrot, Q.
AU - Berteloot, L.
AU - Boddaert, N.
AU - Sarnacki, S.
AU - Bloch, I.
N1 - Publisher Copyright:
© Springer Nature Switzerland AG 2018.
PY - 2018/1/1
Y1 - 2018/1/1
N2 - In this paper, we propose a patch-based deep learning approach to segment pelvic vessels in 3D MRI images of pediatric patients. For a given T2 weighted MRI volume, a set of 2D axial patches are extracted using a limited number of user-selected landmarks. In order to take into account the volumetric information, successive 2D axial patches are combined together, producing a set of pseudo RGB color images. These RGB images are then used as input for a convolutional neural network (CNN), pre-trained on the ImageNet dataset, which results into both segmentation and vessel labeling as veins or arteries. The proposed method is evaluated on 35 MRI volumes of pediatric patients, obtaining an average segmentation accuracy in terms of Average Symmetric Surface Distance of ASSD= 0.89 ± 0.07 mm and Dice Index of DC= 0.79 ± 0.02.
AB - In this paper, we propose a patch-based deep learning approach to segment pelvic vessels in 3D MRI images of pediatric patients. For a given T2 weighted MRI volume, a set of 2D axial patches are extracted using a limited number of user-selected landmarks. In order to take into account the volumetric information, successive 2D axial patches are combined together, producing a set of pseudo RGB color images. These RGB images are then used as input for a convolutional neural network (CNN), pre-trained on the ImageNet dataset, which results into both segmentation and vessel labeling as veins or arteries. The proposed method is evaluated on 35 MRI volumes of pediatric patients, obtaining an average segmentation accuracy in terms of Average Symmetric Surface Distance of ASSD= 0.89 ± 0.07 mm and Dice Index of DC= 0.79 ± 0.02.
U2 - 10.1007/978-3-030-00807-9_10
DO - 10.1007/978-3-030-00807-9_10
M3 - Conference contribution
AN - SCOPUS:85054838020
SN - 9783030008062
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 97
EP - 106
BT - Data Driven Treatment Response Assessment and Preterm, Perinatal, and Paediatric Image Analysis - First International Workshop, DATRA 2018 and Third International Workshop, PIPPI 2018 Held in Conjunction with MICCAI 2018, Proceedings
A2 - Melbourne, Andrew
A2 - Aughwane, Rosalind
A2 - Robinson, Emma
A2 - Licandro, Roxane
A2 - Gau, Melanie
A2 - Kampel, Martin
A2 - DiFranco, Matthew
A2 - Rota, Paolo
A2 - Licandro, Roxane
A2 - Moeskops, Pim
A2 - Schwartz, Ernst
A2 - Makropoulos, Antonios
PB - Springer Verlag
T2 - 1st International Workshop on Data Driven Treatment Response Assessment, DATRA 2018 and 3rd International Workshop on Preterm, Perinatal, and Paediatric Image Analysis, PIPPI 2018 Held in Conjunction with 21st International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2018
Y2 - 16 September 2018 through 16 September 2018
ER -