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.

Dual Energy X-ray Absorptiometry (DXA) is the standard exam for osteoporosis diagnosis and fracture risk evaluation at the spine. However, numerous patients with bone fragility are not diagnosed as such. In fact, standard analysis of DXA images does not differentiate between trabecular and cortical bone; neither specifically assess of the bone density in the vertebral body, which is where most of the osteoporotic fractures occur. Quantitative computed tomography (QCT) is an alternative technique that overcomes limitations of DXA-based diagnosis. However, due to the high cost and radiation dose, QCT is not used for osteoporosis management. We propose a method that provides a 3-D subject-specific shape and density estimation of the lumbar spine from a single anteroposterior (AP) DXA image. A 3-D statistical shape and density model is built, using a training set of QCT scans, and registered onto the AP DXA image so that its projection matches it. Cortical and trabecular bone compartments are segmented using a model-based algorithm. Clinical measurements are performed at different bone compartments. Accuracy was evaluated by comparing DXA-derived to QCT-derived 3-D measurements for a validation set of 180 subjects. The shape accuracy was 1.51 mm at the total vertebra and 0.66 mm at the vertebral body. Correlation coefficients between DXA and QCT-derived measurements ranged from 0.81 to 0.97. The method proposed offers an insightful 3-D analysis of the lumbar spine, which could potentially improve osteoporosis and fracture risk assessment in patients who had an AP DXA scan of the lumbar spine without any additional examination.

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Objective: To assess the relation between three-dimensional (3D) measurements obtained by lumbar dual energy X-ray absorptiometry (DXA) and osteoporotic fractures in dorsal vertebrae. Material and methods: We analysed retrospectively 32 postmenopausal women, allocated to two groups: 16 women in the experimental group, who presented incident fractures of the dorsal vertebrae, and 16 women in the control group, who did not show any type of fracture. Measurements of the (aBMD) of vertebrae L1 through L4 were taken at the initial visit (i.e., prior to the fracture event) by lumbar dual-energy x-ray absorptiometries (DXA). 3D measurements obtained by DXA were evaluated using 3D modelling software (3D-SHAPER). Volumetric bone mineral density (vBMD) was calculated in the trabecular, cortical and integral bone. Cortical thickness and cortical surface BMD (sBMD) were also measured. Differences in measurements derived from the DXA between the experimental and control groups were assessed using an unpaired Student t-test. The odds ratio (OR) and the area under the receiver operating characteristic curve (AUC) were also determined. Results: In the present age-adjusted case-control study, no significant differences were found between the experimental and control groups in terms of weight (ρ=0.44), height (ρ=0.25) and aBMD (ρ=0.11). However, significant differences (ρ<0.05) were found in the integral and trabecular vBMD and in the cortical sBMD. Trabecular vBMD in the vertebral body was the measure that best discriminated between both groups, with an AUC of 0.733, compared to 0.682 of the aBMD. Conclusion:This study shows the ability of 3D models resultant from lumbar DXAs to discern between subjects with incident fractures in the dorsal vertebrae and control subjects. It is necessary to analyse larger cohorts to establish if these measurements could improve the prediction of fracture risk in clinical practice.