Chongqing University of Technology

About: Chongqing University of Technology is a education organization based out in Chongqing, China. It is known for research contribution in the topics: Microstructure & Magnesium alloy. The organization has 5199 authors who have published 4205 publications receiving 35954 citations. The organization is also known as: Chongqing Institute of Technology.

Identifying the Best Machine Learning Algorithms for Brain Tumor Segmentation, Progression Assessment, and Overall Survival Prediction in the BRATS Challenge

Abstract: Gliomas are the most common primary brain malignancies, with different degrees of aggressiveness, variable prognosis and various heterogeneous histologic sub-regions, i.e., peritumoral edematous/invaded tissue, necrotic core, active and non-enhancing core. This intrinsic heterogeneity is also portrayed in their radio-phenotype, as their sub-regions are depicted by varying intensity profiles disseminated across multi-parametric magnetic resonance imaging (mpMRI) scans, reflecting varying biological properties. Their heterogeneous shape, extent, and location are some of the factors that make these tumors difficult to resect, and in some cases inoperable. The amount of resected tumoris a factor also considered in longitudinal scans, when evaluating the apparent tumor for potential diagnosis of progression. Furthermore, there is mounting evidence that accurate segmentation of the various tumor sub-regions can offer the basis for quantitative image analysis towards prediction of patient overall survival. This study assesses thestate-of-the-art machine learning (ML) methods used for brain tumor image analysis in mpMRI scans, during the last seven instances of the International Brain Tumor Segmentation (BraTS) challenge, i.e., 2012-2018. Specifically, we focus on i) evaluating segmentations of the various glioma sub-regions in pre-operative mpMRI scans, ii) assessing potential tumor progression by virtue of longitudinal growth of tumor sub-regions, beyond use of the RECIST/RANO criteria, and iii) predicting the overall survival from pre-operative mpMRI scans of patients that underwent gross tota lresection. Finally, we investigate the challenge of identifying the best ML algorithms for each of these tasks, considering that apart from being diverse on each instance of the challenge, the multi-institutional mpMRI BraTS dataset has also been a continuously evolving/growing dataset.

Ultralong room temperature phosphorescence from amorphous organic materials toward confidential information encryption and decryption.

Abstract: Ultralong room temperature phosphorescence (URTP) emitted from pure amorphous organic molecules is very rare. Although a few crystalline organic molecules could realize URTP with long lifetimes (>100 ms), practical applications of these crystalline organic phosphors are still challenging because the formation and maintenance of high-quality crystals are very difficult and complicated. Herein, we present a rational design for minimizing the vibrational dissipation of pure amorphous organic molecules to achieve URTP. By using this strategy, a series of URTP films with long lifetimes and high phosphorescent quantum yields (up to 0.75 s and 11.23%, respectively) were obtained from amorphous organic phosphors without visible fluorescence and phosphorescence under ambient conditions. On the basis of the unique features of URTP films, a new green screen printing technology without using any ink was developed toward confidential information encryption and decryption. This work presents a breakthrough strategy in applying amorphous organic materials for URTP.