Alpha shape theory for 3D visualization and volumetric measurement of brain tumor progression using magnetic resonance images

Automated segmentation of craniopharyngioma on MR images using U-Net-based deep convolutional neural network

OBJECTIVES

To develop a U-Net-based deep learning model for automated segmentation of craniopharyngioma.

METHODS

A total number of 264 patients diagnosed with craniopharyngiomas were included in this research. Pre-treatment MRIs were collected, annotated, and used as ground truth to learn and evaluate the deep learning model. Thirty-eight patients from another institution were used for independently external testing. The proposed segmentation model was constructed based on a U-Net architecture. Dice similarity coefficients (DSCs), Hausdorff distance of 95% percentile (95HD), Jaccard value, true positive rate (TPR), and false positive rate (FPR) of each case were calculated. One-way ANOVA analysis was used to investigate if the model performance was associated with the radiological characteristics of tumors.

RESULTS

The proposed model showed a good performance in segmentation with average DSCs of 0.840, Jaccard of 0.734, TPR of 0.820, FPR of 0.000, and 95HD of 3.669 mm. It performed feasibly in the independent external test set, with average DSCs of 0.816, Jaccard of 0.704, TPR of 0.765, FPR of 0.000, and 95HD of 4.201 mm. Also, one-way ANOVA suggested the performance was not statistically associated with radiological characteristics, including predominantly composition (p = 0.370), lobulated shape (p = 0.353), compressed or enclosed ICA (p = 0.809), and cavernous sinus invasion (p = 0.283).

CONCLUSIONS

The proposed deep learning model shows promising results for the automated segmentation of craniopharyngioma.

KEY POINTS

• The segmentation model based on U-Net showed good performance in segmentation of craniopharyngioma. • The proposed model showed good performance regardless of the radiological characteristics of craniopharyngioma. • The model achieved feasibility in the independent external dataset obtained from another center.

Near-infrared upconversion multimodal nanoparticles for targeted radionuclide therapy of breast cancer lymphatic metastases

The theranostics of lymph node metastasis has always been one of the major obstacles to defeating breast cancer and an important decisive factor in the prognosis of patients. Herein, we design NaGdF₄:Yb,Tm@NaLuF₄ upconversion nanoparticles with PEG and anti-HER2 monoclonal antibody (trastuzumab, Herceptin) (NP-mAb), the delivery of NP-mAb through the lymphatic system allows for effective targeting and accumulation in lymphatic metastasis. Combination of radionuclides ⁶⁸Ga and ¹⁷⁷Lu could be chelated by the bisphosphate groups of NP-mAb. The obtained nanoprobe (NP-mAb) and nanonuclear drug (⁶⁸Ga-NP-mAb or ¹⁷⁷Lu-NP-mAb) exhibited excellent stability and show high accumulation and prolong retention in the lymph node metastasis after intratumoral injection into the foot pad by near-infrared fluorescence (NIRF), single-photon emission computed tomography (SPECT) and positron emission tomography (PET) imaging. Utilizing the β-rays released by ¹⁷⁷Lu, ¹⁷⁷Lu-NP-mAb could not only decrease the incidence of lymph node metastasis, but also significantly decrease the volumes of lymph node metastasis. Additionally, ¹⁷⁷Lu-NP-mAb induce no obvious toxicity to treated mice through blood routine, liver and kidney function assay. Therefore, nanoprobe and nanonuclear drug we designed could be acted as excellent theranostics agents for lymph node metastasis, providing potential alternatives diagnose and treatment option for lymph node metastasis.

3D Reconstruction Methods Purporting 3D Visualization and Volume Estimation of Brain Tumors

This work proposes the Crust algorithm for 3D reconstruction of brain tumor, an effective mechanism in the visualization of tumors for presurgical planning, radiation dose calculation. Despite the promising performance of Crust algorithm in reconstruction of Stanford models, it has not yet been considered in 3D reconstruction of brain tumor. Validation of the results is done using the comparison of the 3D models from two cutting edge techniques namely the Marching Cube and the Alpha shape algorithm. The obtained result shows that Crust algorithm provides the brain tumor model with an average quality of triangle meshes ranging from 0.85 to 0.95. Concerning the visual realism, the quality of Crust algorithm models is higher on comparison to the other models. Precision of tumor volume measurement by convex hull method is analysed by repeatability and reproducibility. The standard deviations of repeatability were between 2.03 % and 3.97 %. The experimental results show that Linear Crust algorithm produces high quality meshes with average quality of equilateral triangles close to 1.

2D alpha-shapes to quantify retinal microvasculature morphology and their application to proliferative diabetic retinopathy characterisation in fundus photographs

The use of 2D alpha-shapes (α-shapes) to quantify morphological features of the retinal microvasculature could lead to imaging biomarkers for proliferative diabetic retinopathy (PDR). We tested our approach using the MESSIDOR dataset that consists of colour fundus photographs from 547 healthy individuals, 149 with mild diabetic retinopathy (DR), 239 with moderate DR, 199 pre-PDR and 53 PDR. The skeleton (centrelines) of the automatically segmented retinal vasculature was represented as an α-shape and the proposed parameters, complexity (\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${Op\alpha }_{min}$$\end{document}Opαmin), spread (OpA), global shape (VS) and presence of abnormal angiogenesis (Grad_α) were computed. In cross-sectional analysis, individuals with PDR had a lower \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${Op\alpha }_{min}$$\end{document}Opαmin, OpA and Grad_α indicating a vasculature that is more complex, less spread (i.e. dense) and the presence of numerous small vessels. The results show that α-shape parameters characterise vascular abnormalities predictive of PDR (AUC 0.73; 95% CI [0.73 0.74]) and have therefore potential to reveal changes in retinal microvascular morphology.

MATLAB Virtual Toolbox for Retrospective Rockfall Source Detection and Volume Estimation Using 3D Point Clouds: A Case Study of a Subalpine Molasse Cliff

The use of 3D point clouds to improve the understanding of natural phenomena is currently applied in natural hazard investigations, including the quantification of rockfall activity. However, 3D point cloud treatment is typically accomplished using nondedicated (and not optimal) software. To fill this gap, we present an open-source, specific rockfall package in an object-oriented toolbox developed in the MATLAB® environment. The proposed package offers a complete and semiautomatic 3D solution that spans from extraction to identification and volume estimations of rockfall sources using state-of-the-art methods and newly implemented algorithms. To illustrate the capabilities of this package, we acquired a series of high-quality point clouds in a pilot study area referred to as the La Cornalle cliff (West Switzerland), obtained robust volume estimations at different volumetric scales, and derived rockfall magnitude–frequency distributions, which assisted in the assessment of rockfall activity and long-term erosion rates. An outcome of the case study shows the influence of the volume computation on the magnitude–frequency distribution and ensuing erosion process interpretation.

Mobile LiDAR Scanning System Combined with Canopy Morphology Extracting Methods for Tree Crown Parameters Evaluation in Orchards

To meet the demand for canopy morphological parameter measurements in orchards, a mobile scanning system is designed based on the 3D Simultaneous Localization and Mapping (SLAM) algorithm. The system uses a lightweight LiDAR-Inertial Measurement Unit (LiDAR-IMU) state estimator and a rotation-constrained optimization algorithm to reconstruct a point cloud map of the orchard. Then, Statistical Outlier Removal (SOR) filtering and European clustering algorithms are used to segment the orchard point cloud from which the ground information has been separated, and the k-nearest neighbour (KNN) search algorithm is used to restore the filtered point cloud. Finally, the height of the fruit trees and the volume of the canopy are obtained by the point cloud statistical method and the 3D alpha-shape algorithm. To verify the algorithm, tracked robots equipped with LIDAR and an IMU are used in a standardized orchard. Experiments show that the system in this paper can reconstruct the orchard point cloud environment with high accuracy and can obtain the point cloud information of all fruit trees in the orchard environment. The accuracy of point cloud-based segmentation of fruit trees in the orchard is 95.4%. The R² and Root Mean Square Error (RMSE) values of crown height are 0.93682 and 0.04337, respectively, and the corresponding values of canopy volume are 0.8406 and 1.5738, respectively. In summary, this system achieves a good evaluation result of orchard crown information and has important application value in the intelligent measurement of fruit trees.

Dual-fiber microfluidic chip for multimodal manipulation of single cells

On-chip single-cell manipulation is imperative in cell biology and it is desirable for a microfluidic chip to have multimodal manipulation capability. Here, we embedded two counter-propagating optical fibers into the microfluidic chip and configured their relative position in space to produce different misalignments. By doing so, we demonstrated multimodal manipulation of single cells, including capture, stretching, translation, orbital revolution, and spin rotation. The rotational manipulation can be in-plane or out-of-plane, providing flexibility and capability to observe the cells from different angles. Based on out-of-plane rotation, we performed a 3D reconstruction of cell morphology and extracted its five geometric parameters as biophysical features. We envision that this type of microfluidic chip configured with dual optical fibers can be helpful in manipulating cells as the upstream process of single-cell analysis.

3D Visualization of Brain Tumors Using MR Images: A Survey

BACKGROUND

Three-Dimensional visualization of brain tumors is very useful in both diagnosis and treatment stages of brain cancer.

DISCUSSION

It helps the oncologist/neurosurgeon to take the best decision in Radiotherapy and/or surgical resection techniques. 3D visualization involves two main steps; tumor segmentation and 3D modeling.

CONCLUSION

In this article, we illustrate the most widely used segmentation and 3D modeling techniques for brain tumors visualization. We also survey the public databases available for evaluation of the mentioned techniques.

3D cell electrorotation and imaging for measuring multiple cellular biophysical properties

3D rotation is one of many fundamental manipulations to cells and imperative in a wide range of applications in single cell analysis involving biology, chemistry, physics and medicine. In this article, we report a dielectrophoresis-based, on-chip manipulation method that can load and rotate a single cell for 3D cell imaging and multiple biophysical property measurements. To achieve this, we trapped a single cell in constriction and subsequently released it to a rotation chamber formed by four sidewall electrodes and one transparent bottom electrode. In the rotation chamber, rotating electric fields were generated by applying appropriate AC signals to the electrodes for driving the single cell to rotate in 3D under control. The rotation spectrum for in-plane rotation was used to extract the cellular dielectric properties based on a spherical single-shell model, and the stacked images of out-of-plane cell rotation were used to reconstruct the 3D cell morphology to determine its geometric parameters. We have tested the capabilities of our method by rotating four representative mammalian cells including HeLa, C3H10, B lymphocyte, and HepaRG. Using our device, we quantified the area-specific membrane capacitance and cytoplasm conductivity for the four cells, and revealed the subtle difference of geometric parameters (i.e., surface area, volume, and roughness) by 3D cell imaging of cancer cells and normal leukocytes. Combining microfluidics, dielectrophoresis, and microscopic imaging techniques, our electrorotation-on-chip (EOC) technique is a versatile method for manipulating single cells under investigation and measuring their multiple biophysical properties.