Standard

A Hybrid Deep Learning Framework for Integrated Segmentation and Registration: Evaluation on Longitudinal White Matter Tract Changes. / Li, Bo; Niessen, Wiro; Klein, Stefan; de Groot, Marius; Arfan Ikram, M.; Vernooij, Meike; Bron, Esther E.

Medical Image Computing and Computer Assisted Intervention – MICCAI 2019 . ed. / Dinggang Shen; Tianming Liu; Terry M. Peters; Lawrence H. Staib; Caroline Essert; Sean Zhou; Pew-Thian Yap; Ali Khan. Springer, 2019. p. 645-653 (Lecture Notes in Computer Science ; Vol. 11766).

Research output: Chapter in Book/Conference proceedings/Edited volumeConference contributionScientificpeer-review

Harvard

Li, B, Niessen, W, Klein, S, de Groot, M, Arfan Ikram, M, Vernooij, M & Bron, EE 2019, A Hybrid Deep Learning Framework for Integrated Segmentation and Registration: Evaluation on Longitudinal White Matter Tract Changes. in D Shen, T Liu, TM Peters, LH Staib, C Essert, S Zhou, P-T Yap & A Khan (eds), Medical Image Computing and Computer Assisted Intervention – MICCAI 2019 . Lecture Notes in Computer Science , vol. 11766, Springer, pp. 645-653, Medical Image Computing and Computer Assisted Intervention – MICCAI 2019, Shenzhen, China, 13/10/19. https://doi.org/10.1007/978-3-030-32248-9-72

APA

Li, B., Niessen, W., Klein, S., de Groot, M., Arfan Ikram, M., Vernooij, M., & Bron, E. E. (2019). A Hybrid Deep Learning Framework for Integrated Segmentation and Registration: Evaluation on Longitudinal White Matter Tract Changes. In D. Shen, T. Liu, T. M. Peters, L. H. Staib, C. Essert, S. Zhou, P-T. Yap, ... A. Khan (Eds.), Medical Image Computing and Computer Assisted Intervention – MICCAI 2019 (pp. 645-653). (Lecture Notes in Computer Science ; Vol. 11766). Springer. https://doi.org/10.1007/978-3-030-32248-9-72

Vancouver

Li B, Niessen W, Klein S, de Groot M, Arfan Ikram M, Vernooij M et al. A Hybrid Deep Learning Framework for Integrated Segmentation and Registration: Evaluation on Longitudinal White Matter Tract Changes. In Shen D, Liu T, Peters TM, Staib LH, Essert C, Zhou S, Yap P-T, Khan A, editors, Medical Image Computing and Computer Assisted Intervention – MICCAI 2019 . Springer. 2019. p. 645-653. (Lecture Notes in Computer Science ). https://doi.org/10.1007/978-3-030-32248-9-72

Author

Li, Bo ; Niessen, Wiro ; Klein, Stefan ; de Groot, Marius ; Arfan Ikram, M. ; Vernooij, Meike ; Bron, Esther E. / A Hybrid Deep Learning Framework for Integrated Segmentation and Registration: Evaluation on Longitudinal White Matter Tract Changes. Medical Image Computing and Computer Assisted Intervention – MICCAI 2019 . editor / Dinggang Shen ; Tianming Liu ; Terry M. Peters ; Lawrence H. Staib ; Caroline Essert ; Sean Zhou ; Pew-Thian Yap ; Ali Khan. Springer, 2019. pp. 645-653 (Lecture Notes in Computer Science ).

BibTeX

@inproceedings{fec4420ed19e4238bf74532d2317d2cb,
title = "A Hybrid Deep Learning Framework for Integrated Segmentation and Registration: Evaluation on Longitudinal White Matter Tract Changes",
abstract = "To accurately analyze changes of anatomical structures in longitudinal imaging studies, consistent segmentation across multiple time-points is required. Existing solutions often involve independent registration and segmentation components. Registration between time-points is used either as a prior for segmentation in a subsequent time point or to perform segmentation in a common space. In this work, we propose a novel hybrid convolutional neural network (CNN) that integrates segmentation and registration into a single procedure. We hypothesize that the joint optimization leads to increased performance on both tasks. The hybrid CNN is trained by minimizing an integrated loss function composed of four different terms, measuring segmentation accuracy, similarity between registered images, deformation field smoothness, and segmentation consistency. We applied this method to the segmentation of white matter tracts, describing functionally grouped axonal fibers, using N = 8045 longitudinal brain MRI data of 3249 individuals. The proposed method was compared with two multistage pipelines using two existing segmentation methods combined with a conventional deformable registration algorithm. In addition, we assessed the added value of the joint optimization for segmentation and registration separately. The hybrid CNN yielded significantly higher accuracy, consistency and reproducibility of segmentation than the multistage pipelines, and was orders of magnitude faster. Therefore, we expect it can serve as a novel tool to support clinical and epidemiological analyses on understanding microstructural brain changes over time.",
keywords = "Simultaneous, Segmentation, Deformable registration, Diffusion MRI, White matter tract, CNN, Longitudinal",
author = "Bo Li and Wiro Niessen and Stefan Klein and {de Groot}, Marius and {Arfan Ikram}, M. and Meike Vernooij and Bron, {Esther E.}",
year = "2019",
doi = "10.1007/978-3-030-32248-9-72",
language = "English",
series = "Lecture Notes in Computer Science",
publisher = "Springer",
pages = "645--653",
editor = "Shen, {Dinggang } and Liu, {Tianming } and Peters, {Terry M. } and Staib, {Lawrence H. } and Essert, {Caroline } and Zhou, {Sean } and Yap, {Pew-Thian } and Khan, {Ali }",
booktitle = "Medical Image Computing and Computer Assisted Intervention – MICCAI 2019",

}

RIS

TY - GEN

T1 - A Hybrid Deep Learning Framework for Integrated Segmentation and Registration: Evaluation on Longitudinal White Matter Tract Changes

AU - Li, Bo

AU - Niessen, Wiro

AU - Klein, Stefan

AU - de Groot, Marius

AU - Arfan Ikram, M.

AU - Vernooij, Meike

AU - Bron, Esther E.

PY - 2019

Y1 - 2019

N2 - To accurately analyze changes of anatomical structures in longitudinal imaging studies, consistent segmentation across multiple time-points is required. Existing solutions often involve independent registration and segmentation components. Registration between time-points is used either as a prior for segmentation in a subsequent time point or to perform segmentation in a common space. In this work, we propose a novel hybrid convolutional neural network (CNN) that integrates segmentation and registration into a single procedure. We hypothesize that the joint optimization leads to increased performance on both tasks. The hybrid CNN is trained by minimizing an integrated loss function composed of four different terms, measuring segmentation accuracy, similarity between registered images, deformation field smoothness, and segmentation consistency. We applied this method to the segmentation of white matter tracts, describing functionally grouped axonal fibers, using N = 8045 longitudinal brain MRI data of 3249 individuals. The proposed method was compared with two multistage pipelines using two existing segmentation methods combined with a conventional deformable registration algorithm. In addition, we assessed the added value of the joint optimization for segmentation and registration separately. The hybrid CNN yielded significantly higher accuracy, consistency and reproducibility of segmentation than the multistage pipelines, and was orders of magnitude faster. Therefore, we expect it can serve as a novel tool to support clinical and epidemiological analyses on understanding microstructural brain changes over time.

AB - To accurately analyze changes of anatomical structures in longitudinal imaging studies, consistent segmentation across multiple time-points is required. Existing solutions often involve independent registration and segmentation components. Registration between time-points is used either as a prior for segmentation in a subsequent time point or to perform segmentation in a common space. In this work, we propose a novel hybrid convolutional neural network (CNN) that integrates segmentation and registration into a single procedure. We hypothesize that the joint optimization leads to increased performance on both tasks. The hybrid CNN is trained by minimizing an integrated loss function composed of four different terms, measuring segmentation accuracy, similarity between registered images, deformation field smoothness, and segmentation consistency. We applied this method to the segmentation of white matter tracts, describing functionally grouped axonal fibers, using N = 8045 longitudinal brain MRI data of 3249 individuals. The proposed method was compared with two multistage pipelines using two existing segmentation methods combined with a conventional deformable registration algorithm. In addition, we assessed the added value of the joint optimization for segmentation and registration separately. The hybrid CNN yielded significantly higher accuracy, consistency and reproducibility of segmentation than the multistage pipelines, and was orders of magnitude faster. Therefore, we expect it can serve as a novel tool to support clinical and epidemiological analyses on understanding microstructural brain changes over time.

KW - Simultaneous

KW - Segmentation

KW - Deformable registration

KW - Diffusion MRI

KW - White matter tract

KW - CNN

KW - Longitudinal

U2 - 10.1007/978-3-030-32248-9-72

DO - 10.1007/978-3-030-32248-9-72

M3 - Conference contribution

T3 - Lecture Notes in Computer Science

SP - 645

EP - 653

BT - Medical Image Computing and Computer Assisted Intervention – MICCAI 2019

A2 - Shen, Dinggang

A2 - Liu, Tianming

A2 - Peters, Terry M.

A2 - Staib, Lawrence H.

A2 - Essert, Caroline

A2 - Zhou, Sean

A2 - Yap, Pew-Thian

A2 - Khan, Ali

PB - Springer

ER -

ID: 66843204