Abstract:Object-based point cloud analysis (OBPA) is useful for information extraction from airborne LiDAR point clouds. An object-based classification method is proposed for classifying the airborne LiDAR point clouds in urban areas herein. In the process of classification, the surface growing algorithm is employed to make clustering of the point clouds without outliers, thirteen features of the geometry, radiometry, topology and echo characteristics are calculated, a support vector machine (SVM) is utilized to classify the segments, and connected component analysis for 3D point clouds is proposed to optimize the original classification results. Three datasets with different point densities and complexities are employed to test our method. Experiments suggest that the proposed method is capable of making a classification of the urban point clouds with the overall classification accuracy larger than 92.34% and the Kappa coefficient larger than 0.8638, and the classification accuracy is promoted with the increasing of the point density, which is meaningful for various types of applications.

In October 2017, the FDA granted regular approval to axicabtagene ciloleucel, a CD19-directed chimeric antigen receptor (CAR) T-cell therapy, for treatment of adult patients with relapsed or refractory large B-cell lymphoma after two or more lines of systemic therapy. Efficacy was based on complete remission (CR) rate and duration of response (DOR) in 101 adult patients with relapsed or refractory large B-cell lymphoma (median 3 prior systemic regimens) treated on a single-arm trial. Patients received a single infusion of axicabtagene ciloleucel, preceded by lymphodepleting chemotherapy with cyclophosphamide and fludarabine. The objective response rate per independent review committee was 72% [95% confidence interval (CI), 62-81], with a CR rate of 51% (95% CI, 41-62). With a median follow-up of 7.9 months, the median DOR was not reached in patients achieving CR (95% CI, 8.1 months; not estimable, NE), whereas patients with partial remission had an estimated median DOR of 2.1 months (95% CI, 1.3-5.3). Among 108 patients evaluated for safety, serious adverse reactions occurred in 52%. Cytokine release syndrome and neurologic toxicities occurred in 94% and 87% of patients, respectively, leading to implementation of a risk evaluation and mitigation strategy.

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Tisagenlecleucel (Kymriah; Novartis Pharmaceuticals) is a CD19-directed genetically modified autologous T-cell immunotherapy. On August 30, 2017, the FDA approved tisagenlecleucel for treatment of patients up to 25 years of age with B-cell precursor acute lymphoblastic leukemia (ALL) that is refractory in second or later relapse. Approval was based on the complete remission (CR) rate, durability of CR, and minimal residual disease (MRD) <0.01% in a cohort of 63 children and young adults with relapsed or refractory ALL treated on a single-arm trial (CCTL019B2202). Treatment consisted of fludarabine and cyclophosphamide followed 2 to 14 days later by a single dose of tisagenlecleucel. The CR rate was 63% (95% confidence interval, 50%-75%), and all CRs had MRD <0.01%. With a median follow-up of 4.8 months, the median duration of response was not reached. Cytokine release syndrome (79%) and neurologic events (65%) were serious toxicities reported in the trial. With implementation of a Risk Evaluation and Mitigation Strategy, the benefit-risk profile was considered acceptable for this patient population with such resistant ALL. A study of safety with 15 years of follow-up is required as a condition of the approval. See related commentary by Geyer, p. 1133 Nonclinical Pharmacology and Toxicology Nonclinical safety studies were conducted with lentivirustransduced T cells prepared from healthy donors and patients in

Abstract:Filtering is one of the core post-processing steps for Airborne Laser Scanning (ALS) point clouds. A segmentation-based filtering (SBF) method is proposed herein. This method is composed of three key steps: point cloud segmentation, multiple echoes analysis, and iterative judgment. Moreover, the third step is our main contribution. Particularly, the iterative judgment is based on the framework of the classic progressive TIN (triangular irregular network) densification (PTD) method, but with basic processing unit being a segment rather than a single point. Seven benchmark datasets provided by ISPRS Working Group III/3 are utilized to test the SBF algorithm and the classic PTD method. Experimental results suggest that, compared with the PTD method, the SBF approach is capable of preserving discontinuities of landscapes and removing the lower parts of large objects attached on the ground surface. As a result, the SBF approach is able to reduce omission errors and total errors by 18.26% and 11.47% respectively, which would significantly decrease the cost of manual operation required in post-processing.

This paper presents an automated and effective method for detecting 3D edges and tracing feature lines from 3D-point clouds. This method is named Analysis of Geometric Properties of Neighborhoods (AGPN), and it includes two main steps: edge detection and feature line tracing. In the edge detection step, AGPN analyzes geometric properties of each query point's neighborhood, and then combines RANdom SAmple Consensus (RANSAC) and angular gap metric to detect edges. In the feature line tracing step, feature lines are traced by a hybrid method based on region growing and model fitting in the detected edges. Our approach is experimentally validated on complex man-made objects and large-scale urban scenes with millions of points. Comparative studies with state-of-the-art methods demonstrate that our method obtains a promising, reliable, and high performance in detecting edges and tracing feature lines in 3D-point clouds. Moreover, AGPN is insensitive to the point density of the input data.

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This paper presents an automated and effective framework for classifying airborne laser scanning (ALS) point clouds. The framework is composed of four stages: (i) step-wise point cloud segmentation, (ii) feature extraction, (iii) Random Forests (RF) based feature selection and classification, and (iv) post-processing. First, a step-wise point cloud segmentation method is proposed to extract three kinds of segments, including planar, smooth and rough surfaces. Second, a segment, rather than an individual point, is taken as the basic processing unit to extract features. Third, RF is employed to select features and classify these segments. Finally, semantic rules are employed to optimize the classification result. Three datasets provided by Open Topography are utilized to test the proposed method. Experiments show that our method achieves a superior classification result with an overall classification accuracy larger than 91.17%, and kappa coefficient larger than 83.79%.