Imperial College London

ProfessorDanielRueckert

Faculty of EngineeringDepartment of Computing

Head of Department of Computing
 
 
 
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Contact

 

+44 (0)20 7594 8333d.rueckert Website

 
 
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Location

 

568Huxley BuildingSouth Kensington Campus

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Summary

 

Publications

Citation

BibTex format

@article{Cerrolaza:2019:10.1016/j.media.2019.04.002,
author = {Cerrolaza, JJ and Picazo, ML and Humbert, L and Sato, Y and Rueckert, D and Ballester, MÁG and Linguraru, MG},
doi = {10.1016/j.media.2019.04.002},
journal = {Med Image Anal},
pages = {44--67},
title = {Computational anatomy for multi-organ analysis in medical imaging: A review.},
url = {http://dx.doi.org/10.1016/j.media.2019.04.002},
volume = {56},
year = {2019}
}

RIS format (EndNote, RefMan)

TY  - JOUR
AB - The medical image analysis field has traditionally been focused on the development of organ-, and disease-specific methods. Recently, the interest in the development of more comprehensive computational anatomical models has grown, leading to the creation of multi-organ models. Multi-organ approaches, unlike traditional organ-specific strategies, incorporate inter-organ relations into the model, thus leading to a more accurate representation of the complex human anatomy. Inter-organ relations are not only spatial, but also functional and physiological. Over the years, the strategies proposed to efficiently model multi-organ structures have evolved from the simple global modeling, to more sophisticated approaches such as sequential, hierarchical, or machine learning-based models. In this paper, we present a review of the state of the art on multi-organ analysis and associated computation anatomy methodology. The manuscript follows a methodology-based classification of the different techniques available for the analysis of multi-organs and multi-anatomical structures, from techniques using point distribution models to the most recent deep learning-based approaches. With more than 300 papers included in this review, we reflect on the trends and challenges of the field of computational anatomy, the particularities of each anatomical region, and the potential of multi-organ analysis to increase the impact of medical imaging applications on the future of healthcare.
AU - Cerrolaza,JJ
AU - Picazo,ML
AU - Humbert,L
AU - Sato,Y
AU - Rueckert,D
AU - Ballester,MÁG
AU - Linguraru,MG
DO - 10.1016/j.media.2019.04.002
EP - 67
PY - 2019///
SP - 44
TI - Computational anatomy for multi-organ analysis in medical imaging: A review.
T2 - Med Image Anal
UR - http://dx.doi.org/10.1016/j.media.2019.04.002
UR - https://www.ncbi.nlm.nih.gov/pubmed/31181343
VL - 56
ER -