Point Distribution Model Research Articles

Deep reinforcement learning and convolutional autoencoders for anomaly detection of congenital inner ear malformations in clinical CT images

Detection of abnormalities within the inner ear is a challenging task even for experienced clinicians. In this study, we propose an automated method for automatic abnormality detection to provide support for the diagnosis and clinical management of various otological disorders. We propose a framework for inner ear abnormality detection based on deep reinforcement learning for landmark detection which is trained uniquely in normative data. In our approach, we derive two abnormality measurements: Dimage and Uimage. The first measurement, Dimage, is based on the variability of the predicted configuration of a well-defined set of landmarks in a subspace formed by the point distribution model of the location of those landmarks in normative data. We create this subspace using Procrustes shape alignment and Principal Component Analysis projection. The second measurement, Uimage, represents the degree of hesitation of the agents when approaching the final location of the landmarks and is based on the distribution of the predicted Q-values of the model for the last ten states. Finally, we unify these measurements in a combined anomaly measurement called Cimage. We compare our method’s performance with a 3D convolutional autoencoder technique for abnormality detection using the patch-based mean squared error between the original and the generated image as a basis for classifying abnormal versus normal anatomies. We compare both approaches and show that our method, based on deep reinforcement learning, shows better detection performance for abnormal anatomies on both an artificial and a real clinical CT dataset of various inner ear malformations with an increase of 11.2% of the area under the ROC curve. Our method also shows more robustness against the heterogeneous quality of the images in our dataset.

The shape of things to come. Mapping spatiotemporal progression of striatal morphology in Huntington disease: The IMAGE-HD study

Mapping the spatiotemporal progression of neuroanatomical change in Huntington's Disease (HD) is fundamental to the development of bio-measures for prognostication. Statistical shape analysis to measure the striatum has been performed in HD, however there have been a limited number of longitudinal studies. To address these limitations, we utilised the Spherical Harmonic Point Distribution Method (SPHARM-PDM) to generate point distribution models of the striatum in individuals, and used linear mixed models to test for localised shape change over time in pre-manifest HD (pre-HD), symp-HD (symp-HD) and control individuals. Longitudinal MRI scans from the IMAGE-HD study were used (baseline, 18 and 30 months). We found significant differences in the shape of the striatum between groups. Significant group-by-time interaction was observed for the putamen bilaterally, but not for caudate. A differential rate of shape change between groups over time was observed, with more significant deflation in the symp-HD group in comparison with the pre-HD and control groups. CAG repeats were correlated with bilateral striatal shape in pre-HD and symp-HD. Robust statistical analysis of the correlates of striatal shape change in HD has confirmed the suitability of striatal morphology as a potential biomarker correlated with CAG-repeat length, and potentially, an endophenotype.

A U-Net with Statistical Shape Restrictions Applied to the Segmentation of the Left Ventricle in Echocardiographic Images

This paper aims to introduce an innovative approach to semantic segmentation by leveraging a convolutional neural network (CNN) for predicting the shape and pose parameters of the left ventricle (LV). Our approach involves a modified U-Net architecture with a regression layer as the final stage, as opposed to the traditional classification layer. This modification allows us to predict all the shape and pose parameters of a statistical shape model, including rotation, translation, scale, and deformation. The adapted U-Net is trained using data from a point distribution model (PDM) of the LV. The experimental results demonstrate a mean Dice coefficient of 0.82 on good quality images, and 0.66 including mean and low-quality images. Our approach successfully overcomes a common issue encountered in CNN-based semantic segmentation. Unlike the inaccurate pixel classification that often leads to unwanted blobs, our CNN generates statistically valid shapes. These shapes hold significant potential in initializing other methods, such as active shape models (ASMs). Our novel CNN-based approach provides a novel solution for semantic segmentation, offering shapes and pose parameters that can enhance the accuracy and reliability of subsequent medical image analysis methods.

Generative compressed breast shape model for digital mammography and digital breast tomosynthesis.

Modelling of the 3D breast shape under compression is of interest when optimizing image processing and reconstruction algorithms for mammography and digital breast tomosynthesis (DBT). Since these imaging techniques require the mechanical compression of the breast to obtain appropriate image quality, many such algorithms make use of breast-like phantoms. However, if phantoms do not have a realistic breast shape, this can impact the validity of such algorithms. To develop a point distribution model of the breast shape obtained through principal component analysis (PCA) of structured light (SL) scans from patient compressed breasts. SL scans were acquired at our institution during routine craniocaudal-view DBT imaging of 236 patients, creating a dataset containing DBT and SL scans with matching information. Thereafter, the SL scans were cleaned, merged, simplified, and set to a regular grid across all cases. A comparison between the initial SL scans after cleaning and the gridded SL scans was performed to determine the absolute difference between them. The scans with points in a regular grid were then used for PCA. Additionally, the correspondence between SL scans and DBT scans was assessed by comparing features such as the chest-to-nipple distance (CND), the projected breast area (PBA) and the length along the chest-wall (LCW). These features were compared using a paired t-test or the Wilcoxon signed rank sum test. Thereafter, the PCA shape prediction and SL scans were evaluated by calculating the mean absolute error to determine whether the model had adequately captured the information in the dataset. The coefficients obtained from the PCA could then parameterize a given breast shape as an offset from the sample means. We also explored correlations of the PCA breast shape model parameters with certain patient characteristics: age, glandular volume, glandular density by mass, total breast volume, compressed breast thickness, compression force, nipple location, and centre of the chest-wall. The median value across cases for the 90th and 99th percentiles of the interpolation error between the initial SL scans after cleaning and the gridded SL scans was 0.50 and 1.16mm, respectively. The comparison between SL and DBT scans resulted in small, but statistically significant, mean differences of 1.6mm, 1.6mm, and 2.2cm2 for the LCW, CND, and PBA, respectively. The final model achieved a median mean absolute error of 0.68mm compared to the scanned breast shapes and a perfect correlation between the first PCA coefficient and the patient breast compressed thickness, making it possible to use it to generate new model-based breast shapes with a specific breast thickness. There is a good agreement between the breast shape coverage obtained with SL scans used to construct our model and the DBT projection images, and we could therefore create a generative model based on this data that is available for download on Github.

The Sleepiness of International Students in Online Chinese Classroom: An Experimental Study Based on Hybrid Technology of Facial Feature Point Distribution Model

The study presented in this article is a ground-breaking investigation into the quick advancement of intelligent online language instruction based on wireless communication networks, learner physiological and psychological alterations;to predict and prompt learners’ sleepiness, a hybrid technology of facial feature point distribution model is applied to the process of online learning of Chinese for international students. This technology aids teachers in timely monitoring and discovering learners’ levels of sleepiness in the process of online learning and adjusting it, ensuring effective teacher-student interaction and the accomplishment of learning objectives in the classroom. The following specific designs are carried out in order to increase the contribution of teaching and training samples to the classification effect: (1) To train the neural network classifier and ensure more stable and accurate recognition results, the system employs the particle swarm optimization approach with adaptive parameter adjustment; (2) A simulated annealing technique is suggested to address the premature convergence flaw of the conventional particle swarm optimization algorithm, and an adjustable inertia weight factor is created to enhance the optimization capability of particle swarm optimization; (3) A PSO-SVM based facial expression recognition system based on multi-scale wavelet transform theory and support vector regression machine is created in order to further enhance the network’s generalization capability. The simulation findings demonstrate some improvement in the convergence of the two widely used face recognition techniques. Additionally, this study provides a principal component analysis approach based on DCT coefficient singular decomposition and a texture feature selection method based on LDA model in an effort to apply wavelet analysis theory to face recognition. Highlights A system for predicting and prompting learners’ sleepiness in online foreign language classroom teaching is constructed In the case of being unable to actively integrate into online classroom teaching, learners are prone to lack of concentration, sleepiness, even anxiety, irritability and facial muscle twitching Eye fatigue identified at a distance of 0.3–1.5 m from ordinary cameras is relatively easy to identify The appearance of a large proportion of tired students in the online classroom suggests that teachers should adjust the teaching design scheme The reasons leading to the burnout of online learners are very complex, and it still needs interdisciplinary in-depth research

Snake Game Using Hand Recognition System

Abstract: This paper presents engineering and execution nuances of a hand's movement furthermore motion acknowledgment framework which has been created at Meerut Institute of Engineering and Technology. The framework utilizes live 15-piece shading video from an arranged camera, runs in genuine time (On a DEC Alpha, 25 edges per second), and adapts well to foundation mess. Following is accomplished utilizing the 2D deformable Active Shape Models (savvy snakes), what's more a genetic calculation is utilized to play out an underlying worldwide picture search. The Point Distribution Model, which was used for both the snakes and the hereditary calculations, is a nonexclusive and extensible model that can be used to follow any 2D deformable object. Keywords: OpenCv, Feature tracking, Saavy Snakes, Genetic Algorithm, Lightning issues.

Deep Reinforcement Learning for Detection of Abnormal Anatomies

Automatic detection of abnormal anatomies or malformations of different structures of the human body is a challenging task that could provide support for clinicians in their daily practice. Compared to normative anatomies, there is a low presence of anatomical abnormalities in patients, and the great variation within malformations make it challenging to design deep learning frameworks for automatic detection. We propose a framework for anatomical abnormality detection, which benefits from using a deep reinforcement learning model for landmark detection trained in normative data. We detect the abnormalities using the variability between the predicted landmarks configurations in a subspace based on a point distribution model of landmarks using Procrustes shape alignment and principal component analysis projection from normative data. We demonstrate the performance of this implementation on clinical CT scans of the inner ear, and show how synthetically created abnormal cochlea anatomy can be detected using the prediction of five landmarks around the cochlea. Our approach shows a Receiver Operating Characteristics (ROC) Area Under The Curve (AUC) of 0.97, and 96% accuracy for the detection of abnormal anatomy on synthetic data.

The Brain Structural-Functional Vulnerability in Drug-Naive Children With Juvenile Idiopathic Arthritis: Insights From the Hippocampus.

ObjectiveLeveraging an integrative multimodal MRI paradigm to elaborate on the hippocampus-derived structural and functional changes in children and adolescents with juvenile idiopathic arthritis (JIA) and to explore potential correlations within the “joint-inflammation-brain” axis during the period of central neural system (CNS) development.MethodsTwenty-one patients with JIA all completed the multimodal MRI scanning, laboratory tests, and neuropsychological assessments; meanwhile, 23 matched controls were recruited. We then harnessed the spherical harmonics with a point distribution model (SPHARM-PDM) and the ROI-to-voxel functional connectivity (FC) to measure the hippocampal shape and hippocampo-cortical FC patterns. Correlation analysis was performed to explore the potential links in neuroimaging features with disease-related indices.ResultsCompared to controls, JIA patients only presented an atrophic tendency in the posterior part of the bilateral hippocampus. The hippocampo-cortical FC revealed the between-group divergences mainly located at the pain matrix, striatum, and temporal lobe. Remarkably, the enhanced FC between the right hippocampus and postcentral cortex is positively correlated with the disability index, while the weakened FC of right anterior hippocampus with right insula and that of left posterior hippocampus with left superior temporal gyrus was inversely related to the erythrocyte sedimentation rate and anxiety status, separately.ConclusionAs with macroscopic damages, the altered functional-connectome patterns of the hippocampus in JIA patients might be more sensitive to detect the early neuropathological changes. Moreover, the functional disturbances were demonstrated associated with the physical disability, inflammation, and emotional status. These findings may enlighten us on the underlying neuropathological mechanism of CNS comorbidities in JIA.

Statistical shape analysis of the tricuspid valve in hypoplastic left heart sydrome.

Hypoplastic left heart syndrome (HLHS) is a congenital heart disease characterized by incomplete development of the left heart. Children with HLHS undergo a series of operations which result in the tricuspid valve (TV) becoming the only functional atrioventricular valve. Some of those patients develop tricuspid regurgitation which is associated with heart failure and death and necessitates further surgical intervention. Repair of the regurgitant TV, and understanding the connections between structure and function of this valve remains extremely challenging. Adult cardiac populations have used 3D echocardiography (3DE) combined with computational modeling to better understand cardiac conditions affecting the TV. However, these structure-function analyses rely on simplistic point-based techniques that do not capture the leaflet surface in detail, nor do they allow robust comparison of shapes across groups. We propose using statistical shape modeling and analysis of the TV using Spherical Harmonic Representation Point Distribution Models (SPHARM-PDM) in order to generate a reproducible representation, which in turn enables high dimensional low sample size statistical analysis techniques such as principal component analysis and distance weighted discrimination. Our initial results suggest that visualization of the differences in regurgitant vs. non-regurgitant valves can precisely locate populational structural differences as well as how an individual regurgitant valve differs from the mean shape of functional valves. We believe that these results will support the creation of modern image-based modeling tools, and ultimately increase the understanding of the relationship between valve structure and function needed to inform and improve surgical planning in HLHS.

The analysis of structural magnetic resonance imaging manifestation of hippocampus based on voxel and spherical harmonic surface morphometry in mesial temporal lobe epilepsy

Objective: To compare the hippocampal volume and local surface morphology changes in patients with mesial temporal lobe (mTLE) using the voxel-based morphometry and spherical harmonic methods respectively. Methods: A total of 66 patients (31 males and 35 females, age range from 17 to 48 (28±8) years) with mTLE and 80 age-and gender-matched controls (38 males and 42 females, age range from 19 to 46 (27±7) years) were retrospectively collected from July 2009 to February 2019 at Jinling hospital.. High resolution structural MRI of the whole brain, three-dimensional T1-weighted data(3DT1) were acquired from each subject. The changes of hippocampal volume and surface morphology were evaluated between mTLE groups and controls for observing the hippocampal atrophy pattern by using voxel-based morphometry and spherical harmonic shape descriptions point distribution model respectively. Pearson correlation analysis was conducted for observing the relationship between the morphological changes of hippocampus and disease duration. Results: Compared with the controls, hippocampal volume on the affected side in patients with mTLE was significantly reduced (Z-score:-1.55±0.57 vs 0.38±0.58, P<0.001) and negatively correlated with disease duration (r=-0.297, P=0.016). Furthermore, surface morphology analysis subtly showed that the atrophy of the affected hippocampus in patients with mTLE mainly located in the head, mesial lateral part and posterior tail of the hippocampus. Their displacement values were negatively correlated with disease duration (r=-0.336, P=0.006) and positively associated with the hippocampal grey matter volume (r=0.336, P=0.006). Conclusions: Voxel-based morphometry analysis reveals a global reduction in hippocampal volume, while the morphological measurement method based on surface shape can describe the local morphological changes of hippocampal atrophy.

Modeling Touch Point Distribution with Rotational Dual Gaussian Model.

Touch point distribution models are important tools for designing touchscreen interfaces. In this paper, we investigate how the finger movement direction affects the touch point distribution, and how to account for it in modeling. We propose the Rotational Dual Gaussian model, a refinement and generalization of the Dual Gaussian model, to account for the finger movement direction in predicting touch point distribution. In this model, the major axis of the prediction ellipse of the touch point distribution is along the finger movement direction, and the minor axis is perpendicular to the finger movement direction. We also propose using projected target width and height, in lieu of nominal target width and height to model touch point distribution. Evaluation on three empirical datasets shows that the new model reflects the observation that the touch point distribution is elongated along the finger movement direction, and outperforms the original Dual Gaussian Model in all prediction tests. Compared with the original Dual Gaussian model, the Rotational Dual Gaussian model reduces the RMSE of touch error rate prediction from 8.49% to 4.95%, and more accurately predicts the touch point distribution in target acquisition. Using the Rotational Dual Gaussian model can also improve the soft keyboard decoding accuracy on smartwatches.

Left ventricle motion estimation for cine MR images using sparse representation with shape constraint.

To propose a left ventricle (LV) motion estimation method based on sparse representation, in order to handle the spatial-varying intensity distortions caused by tissue deformation. For each myocardial landmark, an adaptive dictionary was generated by learning transformations from a training dataset. Then the landmark was tracked using sparse representation. Next, a point distribution model was applied to the overall tracking results. Finally, the dense displacement field of the LV myocardium was estimated based on the correspondence between each landmark. Using the dense displacement field estimated, the circumferential strain was calculated to assess the myocardial function. The performance of the proposed method was quantified by the average perpendicular distance (APD), the Dice metric, and the mean symmetric contour distance (SCD). Comparing to the state-of-the-art techniques, the smallest value of APD and SCD, and the highest value of Dice can be obtained using the proposed method, for three public cardiac datasets. Moreover, the mean value of strain difference between the proposed method and the commercial software Medis Suite MR was -0.01, while the intraclass correlation coefficient between these two methods was 0.91. The proposed method could estimate the dense displacement field of the LV accurately, which outperforms other state-of-the-art techniques. The circumferential strain derived from the proposed method was in excellent agreement with that derived from the Medis Suite MR software, while segmental strain abnormalities were detected for most of the subjects with heart diseases, which indicates the potential of the proposed method for clinical usage.

Automatic segmentation of left and right ventricles in cardiac MRI using 3D-ASM and deep learning

Segmentation of the left and right ventricles in cardiac MRI (Magnetic Resonance Imaging) is a prerequisite step for evaluating global and regional cardiac function. This work presents a novel and robust schema for MRI segmentation by combining the advantages of deep learning localization and 3D-ASM (3D Active Shape Model) restriction without any user interaction. Three fundamental techniques are exploited: (1) manual 2D contours are used to build distance maps to get 3D ground truth shape, (2) derived right ventricle points are employed to rotate the coarse initial shape for a refined bi-ventricle initial estimation, (3) segmentation results from deep learning are utilised to build distance maps for the 3D-ASM matching process to help image intensity modelling. The datasets used for experimenting the cine MRI data are 1000 cases from UK Biobank, 500 subjects are selected to train CNN (Convolution Neural Network) parameters, and the remaining 500 cases are adopted for validation. Specifically, cases are used to rebuild point distribution and image intensity models, and also utilized to train CNN. In addition, the left 500 cases are used to perform the validation experiments. For the segmentation of the RV (Right Ventricle) endocardial contour, LV (Left Ventricle) endo- and epicardial contours, overlap, Jaccard similarity index, Point-to-surface errors and cardiac functional parameters are calculated. Experimental results show that the proposed method has advantages over the previous approaches.

Clothing Style Recognition Method Based on Digital Image Processing

The use of digital image processing technology to identify clothing styles from clothing images has broad application potential in clothing consumption analysis, auxiliary clothing design and identity recognition. In the current clothing style recognition field for feature extraction and classification of clothing contours, the main methods are extreme learning machine classification based on wavelet Fourier descriptor, and Euclidean distance classification based on fusion features. However, these methods also have some shortcomings. Currently, there is still no effective method to extract features and classify clothing contours. In order to solve this problem, this paper is based on the SVM classification method of Fourier contour description features. The contour curvature characteristic point in the figure can be described as the bending characteristic of the clothing contour curve, and its calculation is simple and intuitive, which improves the judgment effect of the similarity performance of the Hausdorff distance. This paper constructs a multi-resolution framework of the new image, uses edge detection algorithms to obtain the edges of each level of resolution image, and expands them into narrow edge bands, and combines the point distribution model with the edge narrow bands obtained by edge detection to perform multi-resolution image ASM search for. This paper proposes an image fusion algorithm based on algebraic multigrid and adaptive block. Because the coarse grid extracted by the algebraic multi-grid method can extract the detailed information of the image to a certain extent, the original image can be reconstructed from the grid data. Experimental research shows that from the overall recognition rate of various clothing styles, the Fourier descriptor has a high recognition rate for each clothing, and the stability of multiple experiments is also good.

Can Facial Pose and Expression Be Separated with Weak Perspective Camera?

Separating facial pose and expression within images requires a camera model for 3D-to-2D mapping. The weak perspective (WP) camera has been the most popular choice; it is the default, if not the only option, in state-of-the-art facial analysis methods and software. WP camera is justified by the supposition that its errors are negligible when the subjects are relatively far from the camera, yet this claim has never been tested despite nearly 20 years of research. This paper critically examines the suitability of WP camera for separating facial pose and expression. First, we theoretically show that WP causes pose-expression ambiguity, as it leads to estimation of spurious expressions. Next, we experimentally quantify the magnitude of spurious expressions. Finally, we test whether spurious expressions have detrimental effects on a common facial analysis application, namely Action Unit (AU) detection. Contrary to conventional wisdom, we find that severe pose-expression ambiguity exists even when subjects are not close to the camera, leading to large false positive rates in AU detection. We also demonstrate that the magnitude and characteristics of spurious expressions depend on the point distribution model used to model the expressions. Our results suggest that common assumptions about WP need to be revisited in facial expression modeling, and that facial analysis software should encourage and facilitate the use of the true camera model whenever possible.

A method to determine the shell layout scheme for equipment battlefield damage tests under artillery fire

In this paper, a new method for determining the shell layout scheme is proposed, which can make the equipment damage data by the battlefield damage test resemble as close as possible the actual combat data. This method is based on the analysis of the impact point distribution and effective damage area of equipment. In order to obtain the position of the impact points, an impact point distribution model under artillery fire was established. Similarly, in order to obtain the effective damage area of equipment, the concepts of generalized damage area and task-based equipment functional damage probability were demonstrated, and the corresponding calculation model was established. Through case analysis, the shell layout scheme was effectively obtained, verifying the correctness of the proposed method.

OrgDyn: feature- and model-based characterization of spatial and temporal organoid dynamics.

Organoid model systems recapitulate key features of mammalian tissues and enable high throughput experiments. However, the impact of these experiments may be limited by manual, non-standardized, static or qualitative phenotypic analysis. OrgDyn is an open-source and modular pipeline to quantify organoid shape dynamics using a combination of feature- and model-based approaches on time series of 2D organoid contour images. Our pipeline consists of (i) geometrical and signal processing feature extraction, (ii) dimensionality reduction to differentiate dynamical paths, (iii) time series clustering to identify coherent groups of organoids and (iv) dynamical modeling using point distribution models to explain temporal shape variation. OrgDyn can characterize, cluster and model differences among unique dynamical paths that define diverse final shapes, thus enabling quantitative analysis of the molecular basis of tissue development and disease. https://github.com/zakih/organoidDynamics (BSD 3-Clause License). Supplementary data are available at Bioinformatics online.

Hybrid method for automatic construction of 3D-ASM image intensity models for left ventricle

Active Shape Models (ASMS) play an important role in model based medical image analysis. They utilize point distribution models (PDMs) in which a priori information is encoded into a template so that the objects to be detected can be represented with a fixed topology. One key element in 3D-ASM is the image intensity model (IIM), which is investigated in this work. We propose a hybrid approach to automatically construct 3D-ASM Intensity Models for the left ventricle. To train the IIM, CNN is adopted to obtain the initial shape for 3D-ASM, distance maps for endo and epicardial contours from ground truth are derived, and using PDM, training shapes can be obtained. The training shapes and cardiac images are then used to train an IIM. 1200 cardiac MRI cases from Hubei Cancer Hospital were used in this study. By comparing point-to-surface errors against a proper gold standard, it demonstrates that large-scale cardiac MRIs can be segmented by 3D models trained under this scheme with fair accuracy. Clinical parameters are calculated using the Bland-Altman analysis, and thus we yield biases of 4.8 ml, 2.19 ml, 2.59 ml, 0.96%, 0.69 g and –2.67 g for LVEDV (LV End-diastolic Volume), LVESV (LV End-systolic Volume), LVSV (LV Stroke volume), LVEF (LV Ejection Fraction), LVM-DP (LV mass in diastolic phase) and LVM-SP (LV mass in systolic phase), respectively.

Region-Based Context Enhanced Network for Robust Multiple Face Alignment

The recent studies for face alignment have involved developing an isolated algorithm on well-cropped face images. It is difficult to obtain the expected input by using an off-the-shelf face detector in practical applications. In this paper, we attempt to bridge between face detection and face alignment by establishing a novel joint multi-task model, which allows us to simultaneously detect multiple faces and their landmarks on a given scene image. In contrast to the pipeline-based framework by cascading separate models, we aim to propose an end-to-end convolutional network by sharing and transform feature representations between the task-specific modules. To learn a robust landmark estimator for unconstrained face alignment, three types of context enhanced blocks are designed to encode feature maps with multi-level context, multi-scale context, and global context. In the post-processing step, we develop a shape reconstruction algorithm based on point distribution model to refine the landmark outliers. Extensive experiments demonstrate that our results are robust for the landmark location task and insensitive to the location of estimated face regions. Furthermore, our method significantly outperforms recent state-of-the-art methods on several challenging datasets including 300 W, AFLW, and COFW.

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

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.