Automated clinical target volume delineation using deep 3D neural networks in radiation therapy of Non-small Cell Lung Cancer.

BACKGROUND AND PURPOSE: Clinical targeted volume (CTV) delineation accounting for the patient-specific microscopic tumor spread can be a difficult step in defining the treatment volume. We developed an intelligent and automated CTV delineation system for locally advanced non-small cell lung carcinoma (NSCLC) to cover the microscopic tumor spread while avoiding organs-at-risk (OAR). MATERIALS AND METHODS: A 3D UNet with a customized loss function was used, which takes both the patients' respiration-correlated ("4D") CT scan and the physician contoured internal gross target volume (iGTV) as inputs, and outputs the CTV delineation. Among the 84 identified patients, 60 were randomly selected to train the network, and the remaining as testing. The model performance was evaluated and compared with cropped expansions using the shape similarities to the physicians' contours (the ground-truth) and the avoidance of critical OARs. RESULTS: On the testing datasets, all model-predicted CTV contours followed closely to the ground truth, and were acceptable by physicians. The average dice score was 0.86. Our model-generated contours demonstrated better agreement with the ground-truth than the cropped 5 mm/8 mm expansion method (median of median surface distance of 1.0 mm vs 1.9 mm/2.0 mm), with a small overlap volume with OARs (0.4 cm3 for the esophagus and 1.2 cm3 for the heart). CONCLUSIONS: The CTVs generated by our CTV delineation system agree with the physician's contours. This approach demonstrates the capability of intelligent volumetric expansions with the potential to be used in clinical practice.

Quantitative evaluation of drug-induced microvascular constriction in mice kidney using a novel tool for 3D geometrical analysis of ex vivo organ vasculature.

The analysis of organ vasculature, and more specifically organ microvasculature, carries special importance for toxicological sciences, and especially for evaluation of drug-induced vascular toxicity. This field presents a special challenge in nonclinical drug safety assessments since there are currently no reliable microvascular toxicity biomarkers. Therefore, we aimed to systematically investigate the use of microvascular 3D geometrical analysis of corrosion casts for evaluation of drug-induced vascular toxicity, utilizing a novel image investigation tool that allows full 3D-quantified geometrical analysis of the entire vascular tree structure. Vascular casts of kidneys from control and low- and high-dose ephedrine/caffeine-treated mice were scanned by a micro CT, and images were processed and analyzed using the Vasculomics™ platform. All evaluations were performed on the kidney cortex. Treatment resulted in a significant and dose-related reduction in overall microvessel density throughout the kidney cortex. This effect was most pronounced for vessels with diameters between 25 µm and 35 µm, and affected mostly vessels located in the superficial part of the kidney cortex. The use of 3D analysis tools in drug-induced vascular toxicity studies allows for very high resolution and characterization of drug effects on the microvasculature and can be used as a valuable tool in drug safety assessments.

Free-form object reconstruction from silhouettes, occluding edges and texture edges: a unified and robust operator based on duality.

In this paper, the duality in differential form is developed between a 3D primal surface and its dual manifold formed by the surface's tangent planes, i.e., each tangent plane of the primal surface is represented as a four-dimensional vector which constitutes a point on the dual manifold. The iterated dual theorem shows that each tangent plane of the dual manifold corresponds to a point on the original 3D surface, i.e., the dual of the dual goes back to the primal. This theorem can be directly used to reconstruct 3D surface from image edges by estimating the dual manifold from these edges. In this paper we further develop the work in our original conference papers resulting in the robust differential dual operator. We argue that the operator makes good use of the information available in the image data, by using both points of intensity discontinuity and their edge directions; we provide a simple physical interpretation of what the abstract algorithm is actually estimating and why it makes sense in terms of estimation accuracy; our algorithm operates on all edges in the images, including silhouette edges, self occlusion edges, and texture edges, without distinguishing their types (thus resulting in improved accuracy and handling locally concave surface estimation if texture edges are present); the algorithm automatically handles various degeneracies; and the algorithm incorporates new methodologies for implementing the required operations such as appropriately relating edges in pairs of images, evaluating and using the algorithm's sensitivity to noise to determine the accuracy of an estimated 3D point. Experiments with both synthetic and real images demonstrate that the operator is accurate, robust to degeneracies and noise, and general for reconstructing free-form objects from occluding edges and texture edges detected in calibrated images or video sequences.