Thin-plate Spline Research Articles

Clipping Spline: interactive, dynamic 4D volume clipping and analysis based on thin plate spline

Clipping Spline: interactive, dynamic 4D volume clipping and analysis based on thin plate spline

Study on additional stress diffusion characteristics of waste tire artificial foundation

ABSTRACT The study of additional stress diffusion law in the artificial foundation has a significant importance for analysing the overall deformation mechanism of the ‘foundation + underlying soil’ coupled system. Through plate loading tests, the soil pressure within the foundation under various conditions was measured, and the corresponding P-S curves were obtained. By analysing the trend of P-S curves and evolution of the compressive stiffness, the failure mechanism of the Artificial Foundation and single column composite body was explained. The calculation methods for the interface rubber content ratio and modified volumetric rubber content ratio, which quantify the contact relationship between tires and soil and the interlocking relationship between tires, were provided. Through thin plate spline interpolation, an additional stress field was established, which demonstrate the impact of the rubber content on the diffusion shape of the additional stress. The results indicate that sliding of the underlying layer is the primarily failure mode of the double-layer foundation system. The necessary condition for the hoop effect vanishing is the adequacy of lower boundary stiffness. Compared to plain soil, the bearing capacities of the artificial foundations increase by 2.33–5.72 times. Laying geo-textile and geo-grid between layers can effectively enhance the initial compression stiffness and prolong the linear bearing phase. With an increase in rubber content, the additional stress diffusion angle increase by as much as 16.2% to 79.9%. Finally, a stress diffusion function was established by fitting the measured soil pressure values.

Comparying two methods of disparity analysis of the anterior portion of m1 in the gray voles (Rodentia, Arvicolini) in a model sample: frequency analysis of morphotypes and geometric morphometrics

An analysis of disparity of the anterior portion of m1 shape in the gray voles in an artificial sample consisting of 5 groups with different frequency distributions of 5 morphotypes was carried out. Two main approaches were applied: (a) traditional analysis of frequency distribution of discrete morphotypes and (b) geometric morphometrics (GM) based on a strictly quantitative description of the shape of anterior portion of m1. The groups were compared quantitatively: Cavalli-Sforz–Edwards’ coefficient was applied in the first case, while the thin plate spline and Procrustes analyses in the second; their results were analyzed using a number of standard statistical methods (dispersion analysis, discriminant analysis, multidimensional scaling). Disparity within the groups was assessed using Shannon entropy index and averaged Procrustes distance. Traditional and GM approaches were shown to provide very similar results, with GM having a number of important advantages by being strictly quantitative. The second approach is recommended as the basic to replace the traditional one for explorations in disparity of tooth crown shape in the voles.

The shape of the corpus callosum is different in bipolar disorder.

Bipolar disorder (BD) is a recurrent chronic disorder characterised by fluctuations in mood and energy disposition. Diseases could lead to degenerative alterations in brain structures such as corpus callosum (CC). Studies demonstrated that abnormalities in CC are associated with BD symptoms. The present study aims to analyse the CC of the patients with statistical shape analysis (SSA) and compare the findings with healthy controls. Forty-one BD patients and 41 healthy individuals in similar age groups, which included 23 female and 18 male subjects, participated in the study. CC was marked with landmarks on the mid-sagittal images of each individual. The mean 'Procrustes' point was calculated, and shape deformations were analysed with thin-plate spline analysis. Significant differences were observed in the shape of CC between the two groups, where maximum CC deformation was observed in posterior region marks in BD patients. There was no significant difference between the CC area of the BD patients and controls. CC analysis conducted with SSA revealed significant differences between patients and healthy controls. The study findings emphasised the abnormal distribution of white matter in CC and the variable subregional nature of CC in BD patients. This study may enable the development of more targeted and effective treatment strategies by taking into account biological factors and understanding the differences in the brain regions of individuals with BD.

Advancing Hydrologic Modelling Through Bias Correcting Weather Radar Data: The Valgrosina Case Study in the Italian Alps

ABSTRACTThe urgency of understanding the intricate input–output relationships of the hydrologic cycle is amplified by the accelerating climate change impacts in mountain environments. This study focuses on optimising water resource management of a dammed valley in the Central Alps (Northern Italy). The research aims to integrate radar data and precipitation interpolation techniques (TIN, Copula, cumulative distribution function; CDF techniques, inverse distance weighting; IDW, thin plate spline; TPS, ordinary kriging; OK and detrended kriging; DK) into a semi‐distributed hydrologic model, by utilising hourly precipitation data from 22 rain gauges and a composite weather radar product spanning 2010–2020. Two main objectives were pursued: (i) to develop and evaluate various radar precipitation correction methods against a benchmark dataset and (ii) to calibrate and assess the performance of the GEOFrame hydrologic model forced with corrected precipitation input. Point‐based and spatial correction approaches were evaluated against ground measurements through leave‐one‐out tests. The former derives dependence functions between the biased radar series and those of the closest three rain gauges to the target point applying a triangular irregular network. The latter combines deterministic and geospatial interpolations to the rain gauge/radar residuals to derive a corrected surface by incorporating radar values as trends. Precipitation series exceeding the composite scaled score of the benchmark dataset were used as input for hydrologic modelling. The spatial method combining radar values with ordinary kriging provided the best results for both correction and modelling (hourly KGE > 0.75). The spatial approaches proved easier to apply than the point‐based methods. In addition, correcting precipitation significantly improved low‐flow simulation from negative hourly lnNSE to values greater than 0.25. As a further step, given the overall good performance of the spatial methods, they could be used operationally as an ensemble to analyse management scenarios.

Comparison of Inverse Distance Weighted and Thin Plate Spline Interpolation Methods in Projecting the Strength of the West Sumatra Earthquake

The Indonesian archipelago is situated in a highly active geological zone, making it prone to frequent earthquakes. West Sumatra, located on the west coast of central Sumatra, comprises lowland coastal areas and volcanic plateaus formed by the Barisan Mountains, covering a land area of 42,297.30 km² (2.17% of Indonesia's territory). This research aims to determine which interpolation method—Inverse Distance Weighted (IDW) and Thin Plate Spline (TPS)—provides more accurate predictions of earthquake strength in West Sumatra. The dataset consists of 229 earthquake events, divided into 90% for training (206 points) and 10% for testing (23 points). The training data was further subdivided into 80% training data 2 (164 points) and 20% validation data (42 points). The interpolation processes using the IDW and TPS methods were repeated 100 times, with the training 2 and validation data randomly shuffled in each iteration. Visualization of the interpolation results indicated that the earthquake magnitudes ranged from 2.0 to 4.5. Although the Mean Absolute Percentage Error (MAPE) values for the TPS method on the test and validation datasets were 16.42 and 14.29, respectively—slightly lower than the MAPE values for the IDW method—the t-test results showed no statistically significant difference between the two methods. Statistically, there is no significant difference between IDW and TPS in terms of predictive accuracy. However, researchers prefer the IDW method due to its computational efficiency and simplicity. Therefore, IDW is considered the most suitable method for analyzing earthquake strength in the West Sumatra region

The comparative analysis of energy dissipation behaviour of woven and UD para-aramid fabrics

This research examines how impact energy spreads across para-aramid fabrics, comparing woven and unidirectional types, using 2-D Thin Plate Spline analysis. The experiment involved dropping a weighted hemisphere onto fabric samples and measuring their deformation across different layer counts. To assess how yarn orientation affects energy dispersion, panels with 0º/90º and 0º/90º/±45º configurations were tested. By analysing the fabric's shape changes under impact, the study revealed patterns of energy propagation. Calculations of bending energy and expansion factors provided insights into this behaviour. The findings indicate that unidirectional fabrics outperform woven structures in distributing impact energy across their surface. Moreover, incorporating ±45º fibre reinforcements in both fabric types enhances energy dispersion and mitigates impact effects.

Robust Real-Time Cancer Tracking via Dual-Panel X-Ray Images for Precision Radiotherapy.

Respiratory-induced tumor motion presents a critical challenge in lung cancer radiotherapy, potentially impacting treatment precision and efficacy. This study introduces an innovative, deep learning-based approach for real-time, markerless lung tumor tracking utilizing orthogonal X-ray projection images. It incorporates three key components: (1) a sophisticated data augmentation technique combining a hybrid deformable model with 3D thin-plate spline transformation, (2) a state-of-the-art Transformer-based segmentation network for precise tumor boundary delineation, and (3) a CNN regression network for accurate 3D tumor position estimation. We rigorously evaluated this approach using both patient data from The Cancer Imaging Archive and dynamic thorax phantom data, assessing performance across various noise levels and comparing it with current leading algorithms. For TCIA patient data, the average DSC and HD95 values were 0.9789 and 1.8423 mm, respectively, with an average centroid localization deviation of 0.5441 mm. On CIRS phantoms, DSCs were 0.9671 (large tumor) and 0.9438 (small tumor) with corresponding HD95 values of 1.8178 mm and 1.9679 mm. The 3D centroid localization accuracy was consistently below 0.33 mm. The processing time averaged 90 ms/frame. Even under high noise conditions (S2 = 25), errors for all data remained within 1 mm with tracking success rates mostly at 100%. In conclusion, the proposed markerless tracking method demonstrates superior accuracy, noise robustness, and real-time performance for lung tumor localization during radiotherapy. Its potential to enhance treatment precision, especially for small tumors, represents a significant step toward improving radiotherapy efficacy and personalizing cancer treatment.

Applied machine learning: Performance prediction of heat pipe with mesh wick

This study presents an investigation of a heat pipe with a mesh wick, utilizing machine learning (ML) techniques. The model including radial basis function interpolation (RBF), Kriging model (KRG), and the k-nearest neighborhood model (K-NN) were studied and compared. A set of training and validating populations were classified using a k-means clustering technique. The design variable included the geometric shape of heat pipe such as its diameter, the properties and percentage used of working fluid, and the temperature at the evaporator. The prediction case study included the heat transfer rate (q), and total difference temperature between evaporator and condenser section (ΔT). The prediction results found that the ΔT gave the most accurate indicator while the q is passable to applied. The Kriging model proved to be the most accurate, achieving an RMSE of 0.9896 and R2 of 0.9149 for heat transfer rate prediction, and an RMSE of 0.1902 and R2 of 0.9398 for total temperature difference prediction with 90 % training data. The second-best accuracy was achieved by the RBF model, with the linear, thin plate, and cubic spline kernels performing reasonably well.

Spatiotemporal multilevel joint modeling of longitudinal and survival outcomes in end-stage kidney disease.

Individuals with end-stage kidney disease (ESKD) on dialysis experience high mortality and excessive burden of hospitalizations over time relative to comparable Medicare patient cohorts without kidney failure. A key interest in this population is to understand the time-dynamic effects of multilevel risk factors that contribute to the correlated outcomes of longitudinal hospitalization and mortality. For this we utilize multilevel data from the United States Renal Data System (USRDS), a national database that includes nearly all patients with ESKD, where repeated measurements/hospitalizations over time are nested in patients and patients are nested within (health service) regions across the contiguous U.S. We develop a novel spatiotemporal multilevel joint model (STM-JM) that accounts for the aforementioned hierarchical structure of the data while considering the spatiotemporal variations in both outcomes across regions. The proposed STM-JM includes time-varying effects of multilevel (patient- and region-level) risk factors on hospitalization trajectories and mortality and incorporates spatial correlations across the spatial regions via a multivariate conditional autoregressive correlation structure. Efficient estimation and inference are performed via a Bayesian framework, where multilevel varying coefficient functions are targeted via thin-plate splines. The finite sample performance of the proposed method is assessed through simulation studies. An application of the proposed method to the USRDS data highlights significant time-varying effects of patient- and region-level risk factors on hospitalization and mortality and identifies specific time periods on dialysis and spatial locations across the U.S. with elevated hospitalization and mortality risks.

KF-VTON: Keypoints-Driven Flow Based Virtual Try-On Network

Image-based virtual try-on aims to fit a target garment to a reference person. Most existing methods are limited to solving the Garment-To-Person (G2P) try-on task that transfers a garment from a clean product image to the reference person and do not consider the Person-To-Person (P2P) try-on task that transfers a garment from a clothed person image to the reference person, which limits the practical applicability. The P2P try-on task is more challenging due to spatial discrepancies caused by different poses, body shapes, and views between the reference person and the target person. To address this issue, we propose a novel Keypoints-Driven Flow Based Virtual Try-On Network (KF-VTON) for handling both the G2P and P2P try-on tasks. Our KF-VTON has two key innovations: (1) We propose a new keypoints-driven flow based deformation model to warp the garment. This model establishes spatial correspondences between the target garment and reference person by combining the robustness of Thin-plate Spline (TPS) based deformation and the flexibility of appearance flow based deformation. (2) We investigate a powerful Context-aware Spatially Adaptive Normalization (CSAN) generative module to synthesize the final try-on image. Particularly, CSAN integrates rich contextual information with semantic parsing guidance to properly infer unobserved garment appearances. Extensive experiments demonstrate that our KF-VTON is capable of producing photo-realistic and high-fidelity try-on results for the G2P as well as P2P try-on tasks and surpasses previous state-of-the-art methods both quantitatively and qualitatively. Our code is available at https://github.com/OIUIU/KF-VTON .

Deep-UEO: Empowering medical image registration with hybrid strategy based on deep learning and united equilibrium optimizer

Traditional optimization methods encounter challenges in determining the optimal geometric transformation parameters and often lacking to achieve global optimum which leads to extended computational time required for convergence. To address this challenge, a dual-phase strategy is proposed for multimodal biomedical image registration by amalgamating the united equilibrium optimizer (UEO) with Visual Geometry Group (VGG-19). In first phase, the input images are aligned by identifying the optimal values of rigid transformation parameters through UEO, with mutual information (MI) maximization serving as an objective function. In second phase, VGG19 is employed to enhance the reliability of resulting registered image obtained through UEO optimization for extracting both the low- and high-level features of reference and target images. Following this, dynamic inlier selection is employed to refine the matching process, integrating expectation maximization for iterative updating of inlier selection, thus improving registration accuracy. Finally, thin plate spline interpolation is utilized to calculate the transformation matrix, ensuring precise alignment between reference and UEO registered image. Extensive experiments are conducted on multimodal medical images sourced from publicly available repositories and clinical(in-house) datasets to validate the reliability of the proposed scheme. These experiments showcase substantial improvements in RMSE from 22.21094 to 10.983654, SSIM from 0.7190832 to 0.8409321, PSNR from 30.176044 to 34.095471, and CC from 0.9304633 to 0.9809432, outperforming state-of-the-art methods.

Longitudinal registration of thoracic CT images with radiation-induced lung diseases: A divide-and-conquer approach based on component structure wise registration using coherent point drift

Background and ObjectiveRegistration of pulmonary computed tomography (CT) images with radiation-induced lung diseases (RILD) was essential to investigate the voxel-wise relationship between the formation of RILD and the radiation dose received by different tissues. Although various approaches had been developed for the registration of lung CTs, their performances remained clinically unsatisfactory for registration of lung CT images with RILD. The main difficulties arose from the longitudinal change in lung parenchyma, including RILD and volumetric change of lung cancers, after radiation therapy, leading to inaccurate registration and artifacts caused by erroneous matching of the RILD tissues. MethodsTo overcome the influence of the parenchymal changes, a divide-and-conquer approach rooted in the coherent point drift (CPD) paradigm was proposed. The proposed method was based on two kernel ideas. One was the idea of component structure wise registration. Specifically, the proposed method relaxed the intrinsic assumption of equal isotropic covariances in CPD by decomposing a lung and its surrounding tissues into component structures and independently registering the component structures pairwise by CPD. The other was the idea of defining a vascular subtree centered at a matched branch point as a component structure. This idea could not only provide a sufficient number of matched feature points within a parenchyma, but avoid being corrupted by the false feature points resided in the RILD tissues due to globally and indiscriminately sampling using mathematical operators. The overall deformation model was built by using the Thin Plate Spline based on all matched points. ResultsThis study recruited 30 pairs of lung CT images with RILD, 15 of which were used for internal validation (leave-one-out cross-validation) and the other 15 for external validation. The experimental results showed that the proposed algorithm achieved a mean and a mean of maximum 1 % of average surface distances <2 and 8 mm, respectively, and a mean and a maximum target registration error <2 mm and 5 mm on both internal and external validation datasets. The paired two-sample t-tests corroborated that the proposed algorithm outperformed a recent method, the Stavropoulou's method, on the external validation dataset (p < 0.05). ConclusionsThe proposed algorithm effectively reduced the influence of parenchymal changes, resulting in a reasonably accurate and artifact-free registration.

Investigating Different Interpolation Methods for High-Accuracy VTEC Analysis in Ionospheric Research

The dynamic structure of the ionosphere and its changes play an important role in comprehending the natural cycle by linking earth sciences and space sciences. Ionosphere research includes a variety of fields like meteorology, radio wave reflection from the atmosphere, atmospheric anomaly detection, the impact on GNSS (Global Navigation Satellite Systems) signals, the exploration of earthquake precursors, and the formation of the northern lights. To gain further insight into this layer and to monitor variations in the total electron content (TEC), ionospheric maps are created using a variety of data sources, including satellite sensors, GNSS data, and ionosonde data. In these maps, data deficiencies are addressed by using interpolation methods. The objective of this study was to obtain high-accuracy VTEC (Vertical Total Electron Content) information to analyze TEC anomalies as precursors to earthquakes. We propose an innovative approach: employing alternative mathematical surfaces for VTEC calculations, leading to enhanced change analytical interpretation for anomaly detections. Within the scope of the application, the second-degree polynomial method, kriging (point and block model), the radial basis multiquadric, and the thin plate spline (TPS) methods were implemented as interpolation methods. During a 49-day period, the TEC values were computed at three different IGS stations, generating 1176 hourly grids for each interpolation model. As reference data, the ionospheric maps produced by the CODE (Center for Orbit Determination in Europe) Analysis Center were used. This study’s findings showed that, based on statistical values, the TPS model offered more accurate results than other methods. Additionally, it has been observed that the peak values in TEC calculations based on polynomial surfaces are eliminated in TPSs.

Spatiotemporal methods for estimating subsurface ocean thermal response to tropical cyclones

Abstract. Tropical cyclones (TCs), driven by heat exchange between the air and sea, pose a substantial risk to many communities around the world. Accurate characterization of the subsurface ocean thermal response to TC passage is crucial for accurate TC intensity forecasts and an understanding of the role that TCs play in the global climate system. However, that characterization is complicated by the high-noise ocean environment, correlations inherent in spatiotemporal data, relative scarcity of in situ observations, and the entanglement of the TC-induced signal with seasonal signals. We present a general methodological framework that addresses these difficulties, integrating existing techniques in seasonal mean field estimation, Gaussian process modeling, and nonparametric regression into an ANOVA decomposition model. Importantly, we improve upon past work by properly handling seasonality, providing rigorous uncertainty quantification, and treating time as a continuous variable, rather than producing estimates that are binned in time. This ANOVA model is estimated using in situ subsurface temperature profiles from the Argo fleet of autonomous floats through a multistep procedure, which (1) characterizes the upper-ocean seasonal shift during the TC season, (2) models the variability in the temperature observations, and (3) fits a thin-plate spline using the variability estimates to account for heteroskedasticity and correlation between the observations. This spline fit reveals the ocean thermal response to the TC passage. Through this framework, we obtain new scientific insights into the interaction between TCs and the ocean on a global scale, including a three-dimensional characterization of the near-surface and subsurface cooling along the TC storm track and the mixing-induced subsurface warming on the track's right side.

Research on Intelligent Prefabricated Reinforced Concrete Staircase Lifting Point Setting Method Considering Multidimensional Spatial Constraint Characteristics

Prefabricated reinforced concrete staircases (PC staircases) are prefabricated components that are widely used in prefabricated buildings and are used in large quantities. During the production and construction of a PC staircase, the lifting point setting directly affects the construction safety, construction efficiency, and construction quality. In this paper, we analyze the quality problems and safety risks in the design, production, and construction of PC staircases under the constraints of multidimensional spatial characteristics, clarify the key technical difficulties of prefabricated staircase lifting under the multidimensional spatial and temporal constraints, and analyze the factors that should be considered in the setting of lifting points. In this paper, a prefabricated staircase lifting point setting database is established and a thin-plate spline interpolation algorithm is introduced to expand it. Based on the support vector machine algorithm, the process of optimization is carried out for the kernel function scale parameter and penalty factor, and it is concluded that for every increase of two in the number of cross-validation folds, the percentage reduction in minimum RMSE is 9.4%, 17.8%, and 4.2%, respectively, the percentage increase in the optimization time is 39.7%, 61.8%, and 27.3%, respectively, and a PC staircase lifting point setup method based on the small-sample database is proposed. The number of lifting points and lifting point locations of the PC staircase satisfying the multidimensional spatial feature constraints can be obtained by inputting the five design parameters of the PC staircase, namely, the number of treads, the height of the treads, the width of the treads, the width of the staircase, and the weight of the staircase, into the lifting point setup method proposed in this paper. The reliability of the precast reinforced concrete staircase lifting point setting method proposed in this paper when considering the multidimensional spatial constraint characteristics is verified by the precast staircases in deep shafts for assembly construction at the Chongqing metro station.

Weakly Supervised Exaggeration Transfer for Caricature Generation With Cross-Modal Knowledge Distillation.

Caricature generation aims to translate portrait photos into caricatures with exaggerated and hand-drawn artistic styles. Previous methods faced challenges in creating diverse and meaningful exaggeration effects, yielding unsatisfactory and uncontrollable results. To overcome this, we proposed ETCari, a novel weakly supervised exaggeration transfer network. ETCari enables the learning of diverse exaggeration caricature styles from various artists, better meeting individual customization requirements and achieving diversified exaggeration while retaining identity features. Specifically, we use the thin-plate spline control point deformation field as the ground truth, serving as the loss for weakly supervised learning to address the challenge of no labels. We convert input to an intermediate modality for domain adaptation, training a teacher model. Subsequently, we perform cross-modal knowledge distillation to train the student model, simplifying preprocessing during inference and avoiding the impact of face parser errors. Experiments on the WebCaricature dataset demonstrate that ETCari effectively performs exaggeration transfer, generating appealing caricatures.

Improved fusion model for generating hourly fine scale land surface temperature data under all-weather condition

Existing Land Surface Temperature (LST) fusion models encounter some challenges due to missing data, complex weather areas, and rapid land cover changes. To overcome these limitations, we proposed the Integrated SpatioTemporal Fusion Algorithm (ISFAT). ISFAT is developed based on contemporary fusion models but in addition incorporates data from partially contaminated LSTs using the masked weight function. This helps to predict fine-scale LST on prediction date while considering error resulting from landcover changes between the base and prediction date. This algorithm also factors in the calculation of model residuals, which are distributed back to the predicted fine-scale LST using the thin-plate spline function. The fine-scale LST on prediction can thereafter employed for predicting hourly fine-scale LST images by integrating a coarse resolution LST with hourly temporal resolution. Compared to contemporary LST fusion models, ISFAT demonstrates superior performance, with mean average differences of 0.1 K and 0.27 K over SADFAT and STITFM, respectively. Additionally, diurnal LST predictions from ISFAT compare well with air temperatures from automatic weather stations. Notably, on February 18, 2020, ISFAT effectively optimized fine-scale LST for Hong Kong, achieving an RMSE of 3.33 K, despite the limitation of cloud cover in the base date. The newly developed ISFAT could facilitate better LST retrieval over a large spatial coverage under different degrees of cloud contamination.

Quantification of Resection Margin following Sublobar Resection in Lung Cancer Patients through Pre- and Post-Operative CT Image Comparison: Utilizing a CT-Based 3D Reconstruction Algorithm.

Sublobar resection has emerged as a standard treatment option for early-stage peripheral non-small cell lung cancer. Achieving an adequate resection margin is crucial to prevent local tumor recurrence. However, gross measurement of the resection margin may lack accuracy due to the elasticity of lung tissue and interobserver variability. Therefore, this study aimed to develop an objective measurement method, the CT-based 3D reconstruction algorithm, to quantify the resection margin following sublobar resection in lung cancer patients through pre- and post-operative CT image comparison. An automated subvascular matching technique was first developed to ensure accuracy and reproducibility in the matching process. Following the extraction of matched feature points, another key technique involves calculating the displacement field within the image. This is particularly important for mapping discontinuous deformation fields around the surgical resection area. A transformation based on thin-plate spline is used for medical image registration. Upon completing the final step of image registration, the distance at the resection margin was measured. After developing the CT-based 3D reconstruction algorithm, we included 12 cases for resection margin distance measurement, comprising 4 right middle lobectomies, 6 segmentectomies, and 2 wedge resections. The outcomes obtained with our method revealed that the target registration error for all cases was less than 2.5 mm. Our method demonstrated the feasibility of measuring the resection margin following sublobar resection in lung cancer patients through pre- and post-operative CT image comparison. Further validation with a multicenter, large cohort, and analysis of clinical outcome correlation is necessary in future studies.

The Corpus Callosum in Schizoaffective Disorder: A Shape Analysis Study

Objective: The corpus callosum is the largest white matter structure in the human brain that connects the cortical regions of both hemispheres. Diseases could lead to degenerative alterations in brain structures such as the corpus callosum (CC). Studies have associated CC abnormalities with Schizoaffective Disorder (SAD) symptoms. We predicted that there may be differences in the CC, an important structure connecting the two halves of the brain, in patients with SAD. The present study aims to analyze the CC of patients with statistical shape analysis (SSA) and compare the findings with healthy controls. Methods: Thirty-nine SAD patients and 39 healthy individuals (11 females and 28 males) of similar age that included subjects participated in the study. CC landmarks were marked on the mid-sagittal images of each participant. The mean ‘Procrustes’ point was determined, and shape deformations were analyzed with thin plate spline analysis. Results: Significant differences were observed between the shapes of CC in the two groups, and maximum CC deformation was observed in the posterior regions of SAD patients. There was no significant difference between the CC area of the SAD patients and the controls. Conclusion: In the present study, the maximum deformation was observed in the posterior region (isthmus and splenium) and the rostrum of the CC. The first CC region, the rostrum (+genu), connects prefrontal and premotor regions, which are associated with cognitive information (landmarks = 1, 7, 8, 9, 13, 15, and 12). The second area, the splenium, connects temporal and occipital cortical areas. These predominantly have auditory, peripheral, and central visual stimulation functions (landmarks = 5, 3, and 4). The current study could assist future studies on the etiology, diagnosis, and treatment of SAD.