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One important technique of surgical navigation is surface registration that matches coordinates in two different spaces. We proposed a novel method to extract an optimal point cloud in image space corresponding to the point cloud in patient space to improve the registration accuracy. In the hemispherical study, our proposed method demonstrated a reduced surface registration error (SRE) and target registration error (TRE) compared to the conventional method in all cases (number of points, distribution, noise). In the phantom study, the SRE and TRE were low and stable in the proposed method, with SRE reduced by 22 % and TRE by 18 % (p < 0.05). The proposed method was not affected by the number of points, distribution, or noise in the point cloud, and it could improve the registration accuracy without the aid of additional equipment.

Non-rigid cortical registration is an important and challenging task due to the geometric complexity of the human cortex and the high degree of inter-subject variability. A conventional solution is to use a spherical representation of surface properties and perform registration by aligning cortical folding patterns in that space. This strategy produces accurate spatial alignment, but often requires high computational cost. Recently, convolutional neural networks (CNNs) have demonstrated the potential to dramatically speed up volumetric registration. However, due to distortions introduced by projecting a sphere to a 2D plane, a direct application of recent learning-based methods to surfaces yields poor results. In this study, we present SphereMorph, a diffeomorphic registration framework for cortical surfaces using deep networks that addresses these issues. SphereMorph uses a UNet-style network associated with a spherical kernel to learn the displacement field and warps the sphere using a modified spatial transformer layer. We propose a resampling weight in computing the data fitting loss to account for distortions introduced by polar projection, and demonstrate the performance of our proposed method on two tasks, including cortical parcellation and group-wise functional area alignment. The experiments show that the proposed SphereMorph is capable of modeling the geometric registration problem in a CNN framework and demonstrate superior registration accuracy and computational efficiency. The source code of SphereMorph will be released to the public upon acceptance of this manuscript at https://github.com/voxelmorph/spheremorph. •Non-rigid cortical registration is an important and challenging task.•Convolutional neural networks (CNNs) have demonstrated the potential to dramatically speed up volumetric registration.•We present SphereMorph, a diffeomorphic registration framework for cortical surfaces using deep networks.•Our experiments demonstrate superior registration accuracy and computational Efficiency.

Frontal bone morphology is considered informative concerning phylogenetic and taxonomic affinities in hominin fossils. However, evaluating individual frontal bone features, especially macroscopically and with qualitative data, can produce contradictory results. One example is the enigmatic frontal bone from Hahnöfersand, Germany, initially dated to ca. 36 ka. A previous description of this specimen found that it exhibits a mix of modern human- and Neanderthal-like features and interpreted it as a potential hybrid between Homo neanderthalensis and Homo sapiens . In this case study, we apply a nearly landmark-free technique (i.e., surface registration) to reassess Hahnöfersand’s supposed intermediate morphology in the context of a revised chronology of ca. 7.5 ka. A three-dimensional comparative analysis was conducted using a dataset of Neanderthals, Middle Pleistocene European hominins, and a diverse Homo sapiens sample. Principal components analysis, Mahalanobis distances, and pairwise Procrustes distances place Hahnöfersand within the variability of Holocene Homo sapiens , with no evidence of intermediate morphology. These findings support Hahnöfersand’s attribution to recent Homo sapiens and highlight the efficacy of the surface registration method for morphological assessments of taxonomy in isolated fragmentary remains, where interpretation is driven entirely by the preserved morphology and, if available, dating.

The MNI CIVET pipeline for automated extraction of cortical surfaces and evaluation of cortical thickness from in-vivo human MRI has been extended for processing macaque brains. Processing is performed based on the NIMH Macaque Template (NMT), as the reference template, with the anatomical parcellation of the surface following the D99 and CHARM atlases. The modifications needed to adapt CIVET to the macaque brain are detailed. Results have been obtained using CIVET-macaque to process the anatomical scans of the 31 macaques used to generate the NMT and another 95 macaques from the PRIME-DE initiative. It is anticipated that the open usage of CIVET-macaque will promote collaborative efforts in data collection and processing, sharing, and automated analyses from which the non-human primate brain imaging field will advance.

Standard registration algorithms need to be independently applied to each surface to register, following careful pre-processing and hand-tuning. Recently, learning-based approaches have emerged that reduce the registration of new scans to running inference with a previously-trained model. The potential benefits are multifold: inference is typically orders of magnitude faster than solving a new instance of a difficult optimization problem, deep learning models can be made robust to noise and corruption, and the trained model may be re-used for other tasks, e.g. through transfer learning. In this paper, we cast the registration task as a surface-to-surface translation problem, and design a model to reliably capture the latent geometric information directly from raw 3D face scans. We introduce Shape-My-Face (SMF), a powerful encoder-decoder architecture based on an improved point cloud encoder, a novel visual attention mechanism, graph convolutional decoders with skip connections, and a specialized mouth model that we smoothly integrate with the mesh convolutions. Compared to the previous state-of-the-art learning algorithms for non-rigid registration of face scans, SMF only requires the raw data to be rigidly aligned (with scaling) with a pre-defined face template. Additionally, our model provides topologically-sound meshes with minimal supervision, offers faster training time, has orders of magnitude fewer trainable parameters, is more robust to noise, and can generalize to previously unseen datasets. We extensively evaluate the quality of our registrations on diverse data. We demonstrate the robustness and generalizability of our model with in-the-wild face scans across different modalities, sensor types, and resolutions. Finally, we show that, by learning to register scans, SMF produces a hybrid linear and non-linear morphable model. Manipulation of the latent space of SMF allows for shape generation, and morphing applications such as expression transfer in-the-wild. We train SMF on a dataset of human faces comprising 9 large-scale databases on commodity hardware.

Three-dimensional (3D) ear morphology is critical for the design of in-the-ear hearing aids, earphones, transcutaneous auricular vagus nerve stimulation (taVNS) electrodes, and auricular reconstruction, yet most existing ear shape models still rely on manually placed landmarks. Here, a fully annotation-free pipeline is presented for constructing a 3D ear atlas and statistical shape model (SSM) of the auricular bowl from 50 surface meshes. Individual ears are iteratively registered to a current atlas using rigid the iterative closest point (ICP) algorithm followed by a bidirectional thin-plate spline (BiTPS) deformation, and dense surface correspondences are established by nearest-neighbour mapping. Registration quality is quantified using mean and maximum nearest-neighbour distance, symmetric Chamfer-L2 distance and coverage. Furthermore, SSM-derived bowl height and width are validated against manual 3D mesh measurements in Geomagic Design X. Across five atlas iterations, the BiTPS pipeline substantially reduces registration errors and increases coverage, and principal component analysis (PCA) derived dimensions show excellent agreement with manual measurements (Pearson r≥0.98, ICC ≥0.98). The proposed framework yields a stable, anatomically plausible ear atlas and an interpretable low-dimensional SSM without manual landmarks, providing a computational basis for the geometric optimization of ear-related medical and wearable devices.

During the last years a wide range of algorithms and devices have been made available to easily acquire range images. The increasing abundance of depth data boosts the need for reliable and unsupervised analysis techniques, spanning from part registration to automated segmentation. In this context, we focus on the recognition of known objects in cluttered and incomplete 3D scans. Locating and fitting a model to a scene are very important tasks in many scenarios such as industrial inspection, scene understanding, medical imaging and even gaming. For this reason, these problems have been addressed extensively in the literature. Several of the proposed methods adopt local descriptor-based approaches, while a number of hurdles still hinder the use of global techniques. In this paper we offer a different perspective on the topic: We adopt an evolutionary selection algorithm that seeks global agreement among surface points, while operating at a local level. The approach effectively extends the scope of local descriptors by actively selecting correspondences that satisfy global consistency constraints, allowing us to attack a more challenging scenario where model and scene have different, unknown scales. This leads to a novel and very effective pipeline for 3D object recognition, which is validated with an extensive set of experiments and comparisons with recent techniques at the state of the art.

Objective To evaluate the accuracy, reliability, and efficiency of voxel- and surface-based registrations for cone-beam computed tomography (CBCT) mandibular superimposition in adult orthodontic patients. Methods Pre- and post-orthodontic treatment CBCT scans of 27 adult patients were obtained. Voxel- and surface-based CBCT mandibular superimpositions were performed using the mandibular basal bone as a reference. The accuracy of the two methods was evaluated using the absolute mean distance measured. The time that was required to perform the measurements using these methods was also compared. Statistical differences were determined using paired t-tests, and inter-observer reliability was assessed by intraclass correlation coefficients (ICCs). Results The absolute mean distance on seven mandible surface areas between voxel- and surface-based registrations was similar but not significantly different. ICC values of the surface-based registration were 0.918 to 0.990, which were slightly lower than those of voxel-based registration that ranged from 0.984 to 0.996. The time required for voxel-based registration and surface-based registration was 44.6 ± 2.5 s and 252.3 ± 7.1 s, respectively. Conclusions Both methods are accurate and reliable and not significantly different from each other. However, voxel-based registration is more efficient than surface-based registration for CBCT mandibular superimposition.

Surface parameterizations have been widely applied to computer graphics and digital geometry processing. In this paper, we propose a novel stretch energy minimization (SEM) algorithm for the computation of equiareal parameterizations of simply connected open surfaces with very small area distortions and highly improved computational efficiencies. In addition, the existence of nontrivial limit points of the SEM algorithm is guaranteed under some mild assumptions of the mesh quality. Numerical experiments indicate that the accuracy, effectiveness, and robustness of the proposed SEM algorithm outperform the other state-of-the-art algorithms. Applications of the SEM on surface remeshing, registration and morphing for simply connected open surfaces are demonstrated thereafter. Thanks to the SEM algorithm, the computation for these applications can be carried out efficiently and reliably.

The position of cortical areas can be approximately predicted from cortical surface folding patterns. However, there is extensive inter-subject variability in cortical folding patterns, prohibiting a one-to-one mapping of cortical folds in certain areas. In addition, the relationship between cortical area boundaries and the shape of the cortex is variable, and weaker for higher-order cortical areas. Current surface registration techniques align cortical folding patterns using sulcal landmarks or cortical curvature, for instance. The alignment of cortical areas by these techniques is thus inherently limited by the sole use of geometric similarity metrics. Magnetic resonance imaging T1 maps show intra-cortical contrast that reflects myelin content, and thus can be used to improve the alignment of cortical areas. In this article, we present a new symmetric diffeomorphic multi-contrast multi-scale surface registration (MMSR) technique that works with partially inflated surfaces in the level-set framework. MMSR generates a more precise alignment of cortical surface curvature in comparison to two widely recognized surface registration algorithms. The resulting overlap in gyrus labels is comparable to FreeSurfer. Most importantly, MMSR improves the alignment of cortical areas further by including T1 maps. As a first application, we present a group average T1 map at a uniquely high-resolution and multiple cortical depths, which reflects the myeloarchitecture of the cortex. MMSR can also be applied to other MR contrasts, such as functional and connectivity data. •MMSR is a novel multi-contrast multi-scale surface registration algorithm.•MMSR generates a symmetric diffeomorphic transformation in native 3D space.•MMSR performs a more precise alignment in comparison to FreeSurfer.•MMSR can use multiple contrasts, such as T1, to improve cortical alignment.•We present a 0.5mm isotropic group average T1 map at multiple cortical depths.