Details

Autor(en) / Beteiligte

• Cerrolaza, Juan J.
• Picazo, Mirella López
• Humbert, Ludovic
• Sato, Yoshinobu
• Rueckert, Daniel
• Ballester, Miguel Ángel González
• Linguraru, Marius George

Titel

Computational anatomy for multi-organ analysis in medical imaging: A review

Ist Teil von

• Medical image analysis, 2019-08, Vol.56, p.44-67

Ort / Verlag

Netherlands: Elsevier B.V

Erscheinungsjahr

2019

Quelle

Alma/SFX Local Collection

Beschreibungen/Notizen

• •This manuscript presents a thorough review of the state-of-the-art on multi-organ analysis in medical imaging.•More than 300 papers reviewed, discussed, and categorized methodologically and anatomically.•For the first time, this paper proposes a methodology-based classification of the different techniques available for the analysis of multi-organ anatomical complex, from the simple global modelling, to the more sophisticated sequential and multi-resolution techniques.•The different methodologies for multi-organ analysis are classified using the following categorization: global and individual models, coupled deformable models, multi-level models, sequential models, atlas-based models, machine-learning models, graphical models, and articulated models.•The manuscripts also reflects on the trends and challenges of multi-organ analysis, the peculiarities of each anatomical region, and its impact on the future of healthcare 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. [Display omitted]