An optimised radial basis function algorithm for fast non-rigid registration of medical images

Deep-learning-based segmentation using individual patient data on prostate cancer radiation therapy

PURPOSE

Organ-at-risk segmentation is essential in adaptive radiotherapy (ART). Learning-based automatic segmentation can reduce committed labor and accelerate the ART process. In this study, an auto-segmentation model was developed by employing individual patient datasets and a deep-learning-based augmentation method for tailoring radiation therapy according to the changes in the target and organ of interest in patients with prostate cancer.

METHODS

Two computed tomography (CT) datasets with well-defined labels, including contoured prostate, bladder, and rectum, were obtained from 18 patients. The labels of the CT images captured during radiation therapy (CT2nd) were predicted using CT images scanned before radiation therapy (CT1st). From the deformable vector fields (DVFs) created by using the VoxelMorph method, 10 DVFs were extracted when each of the modified CT and CT2nd images were deformed and registered to the fixed CT1st image. Augmented images were acquired by utilizing 110 extracted DVFs and spatially transforming the CT1st images and labels. An nnU-net autosegmentation network was trained by using the augmented images, and the CT2nd label was predicted. A patient-specific model was created for 18 patients, and the performances of the individual models were evaluated. The results were evaluated by employing the Dice similarity coefficient (DSC), average Hausdorff distance, and mean surface distance. The accuracy of the proposed model was compared with those of models trained with large datasets.

RESULTS

Patient-specific models were developed successfully. For the proposed method, the DSC values of the actual and predicted labels for the bladder, prostate, and rectum were 0.94 ± 0.03, 0.84 ± 0.07, and 0.83 ± 0.04, respectively.

CONCLUSION

We demonstrated the feasibility of automatic segmentation by employing individual patient datasets and image augmentation techniques. The proposed method has potential for clinical application in automatic prostate segmentation for ART.

Analysis of deformable distortion in the architecture of leaf xylary vessel elements of Carthamus oxycantha caused by heavy metals stress using image registration

Anatomical study of leaf xylary vessel elements of Carthamus oxycantha under various intensities of lead (Pb) and nickel (Ni) stress (200, 400, 600, and 800 mg Pb(NO₃ )₂ , NiCl₂ ·6H₂ O/kg of the soil) was conducted. The deformations caused due to metal stress were detected using point-based image registration technique. Initially, a set of corresponding feature points called landmarks was selected for warping of two-dimensional microscopic images of deformed/source vessel (stressed) to its normal/target (unstressed) counterpart. The results show that the target registration error is less than 3 mm using real plant image datasets. The stress caused alterations mainly in diameter, size, and shape of the cells. Average cell diameter and average wall diameter of vessels were measured with "Image J." The range of decrease in average cell diameter from 18.566 to 13.1 μm and the range of increase in average wall diameter was from 5.166 to 10.1 μm, with increase in stress factor through 200, 400, 600, and 800 mg Pb(NO₃ )₂ , NiCl₂ ·6H₂ O/kg of the soil. We noted large deformation in the form of shrinkage in cell size and diminution in its diameter. The diminution in diameter and the shrinkage in cell size of vessel cells may be due to the deposition of wall materials. It can be a possible strategy to limit the water flow to overcome the rapid mobility and transportation of the excess amount of metals to safeguard the cellular components from unpleasant consequences of metallic stress.

A computer-based simulation of childbirth using the partial Dirichlet-Neumann contact method with total Lagrangian explicit dynamics on the GPU

During physiological or 'natural' childbirth, the fetal head follows a distinct motion pattern-often referred to as the cardinal movements or 'mechanisms' of childbirth-due to the biomechanical interaction between the fetus and maternal pelvic anatomy. The research presented in this paper introduces a virtual reality-based simulation of physiological childbirth. The underpinning science is based on two numerical algorithms including the total Lagrangian explicit dynamics method to calculate soft tissue deformation and the partial Dirichlet-Neumann contact method to calculate the mechanical contact interaction between the fetal head and maternal pelvic anatomy. The paper describes the underlying mathematics and algorithms of the solution and their combination into a computer-based implementation. The experimental section covers first a number of validation experiments on simple contact mechanical problems which is followed by the main experiment of running a virtual reality childbirth. Realistic mesh models of the fetus, bony pelvis and pelvic floor muscles were subjected to the intra-uterine expulsion forces which aim to propel the virtual fetus through the virtual birth canal. Following a series of simulations, taking variations in the shape and size of the geometric models into account, we consistently observed the cardinal movements in the simulator just as they happen in physiological childbirth. The results confirm the potential of the simulator as a predictive tool for problematic childbirths subject to patient-specific adaptations.

Strain-based regional traumatic brain injury intensity in controlled cortical impact: a systematic numerical analysis

Regional strain-based brain injury intensity during controlled cortical impact (CCI) was studied using a three-dimensional numerical rat brain model. A full factorial design of CCI computer experiments was performed using two typical impactor shapes (flat or hemispherical) at a fixed impact velocity of 4?m/s with various impact depths (1, 1.5, 1.6, 2, 2.5, 2.7, and 3?mm) and various impactor diameters (4, 5, 6, 8, and 9.5?mm). In total, 70 CCI cases were simulated numerically. Two injury assessment measures, the cumulative strain damage measure (CSDM), which accounts for the volume of brain tissue with elevated strains, and cumulative strain damage percentage measure (CSDPM), which is a strain-based estimate of the neuronal cell loss percentage, were used to evaluate the risk of brain injury. Results demonstrated positive nonlinear relationships between impact depth and these injury assessment measures in six regions of interest: ipsilateral cortex, ipsilateral corpus callosum, ipsilateral hippocampus, ipsilateral thalamus, cerebellum, and brainstem. However, the impactor diameter was not always positively correlated with regional tissue strains. For the flat impactor group, the 5?mm diameter impactor induced more tissue strain in the corpus callosum/hippocampus, and a smaller impactor induced more strain in the thalamus. For the hemispherical impactor group, a larger impactor tended to induce more tissue strain in subcortical regions, with the exception of the 6?mm diameter impactor. This study systematically predicts regional intensity of primary brain injury according to tissue strain distributions in the hope that strain distribution maps may become a common platform to compare CCI severities with different configurations.