Where is VALDO? VAscular Lesions Detection and segmentatiOn challenge at MICCAI 2021.

Imaging markers of cerebral small vessel disease provide valuable information on brain health, but their manual assessment is time-consuming and hampered by substantial intra- and interrater variability. Automated rating may benefit biomedical research, as well as clinical assessment, but diagnostic reliability of existing algorithms is unknown. Here, we present the results of the VAscular Lesions DetectiOn and Segmentation (Where is VALDO?) challenge that was run as a satellite event at the international conference on Medical Image Computing and Computer Aided Intervention (MICCAI) 2021. This challenge aimed to promote the development of methods for automated detection and segmentation of small and sparse imaging markers of cerebral small vessel disease, namely enlarged perivascular spaces (EPVS) (Task 1), cerebral microbleeds (Task 2) and lacunes of presumed vascular origin (Task 3) while leveraging weak and noisy labels. Overall, 12 teams participated in the challenge proposing solutions for one or more tasks (4 for Task 1-EPVS, 9 for Task 2-Microbleeds and 6 for Task 3-Lacunes). Multi-cohort data was used in both training and evaluation. Results showed a large variability in performance both across teams and across tasks, with promising results notably for Task 1-EPVS and Task 2-Microbleeds and not practically useful results yet for Task 3-Lacunes. It also highlighted the performance inconsistency across cases that may deter use at an individual level, while still proving useful at a population level.

A Multi-Atlas-Based [18F]9-Fluoropropyl-(+)-Dihydrotetrabenazine Positron Emission Tomography Image Segmentation Method for Parkinson's Disease Quantification.

Objectives: [18F]9-fluoropropyl-(+)-dihydrotetrabenazine ([18F]-FP-DTBZ) positron emission tomography (PET) provides reliable information for the diagnosis of Parkinson's disease (PD). In this study, we proposed a multi-atlas-based [18F]-FP-DTBZ PET image segmentation method for PD quantification assessment. Methods: A total of 99 subjects from Xuanwu Hospital of Capital Medical University were included in this study, and both brain PET and magnetic resonance (MR) scans were conducted. Data from 20 subjects were used to generate atlases, based on which a multi-atlas-based [18F]-FP-DTBZ PET segmentation method was developed especially for striatum and its subregions. The proposed method was compared with the template-based method through striatal subregion parcellation performance and the standard uptake value ratio (SUVR) quantification accuracy. Discriminant analysis between healthy controls (HCs) and PD patients was further performed. Results: Segmentation results of the multi-atlas-based method showed better consistency than the template-based method with the ground truth, yielding a dice coefficient of 0.81 over 0.73 on the full striatum. The SUVRs calculated by the multi-atlas-based method had an average interclass correlation coefficient (ICC) of 0.953 with the standardized result, whereas the template-based method only reached 0.815. The SUVRs of HCs were generally higher than that of patients with PD and showed significant differences in all of the striatal subregions (all p < 0.001). The median and posterior putamen performed best in discriminating patients with PD from HCs. Conclusion: The proposed multi-atlas-based [18F]-FP-DTBZ PET image segmentation method achieved better performance than the template-based method, indicating great potential in improving accuracy and efficiency for PD diagnosis in clinical routine.

A Training Data Set Cleaning Method by Classification Ability Ranking for the k -Nearest Neighbor Classifier.

The k -nearest neighbor (KNN) rule is a successful technique in pattern classification due to its simplicity and effectiveness. As a supervised classifier, KNN classification performance usually suffers from low-quality samples in the training data set. Thus, training data set cleaning (TDC) methods are needed for enhancing the classification accuracy by cleaning out noisy, or even wrong, samples in the original training data set. In this paper, we propose a classification ability ranking (CAR)-based TDC method to improve the performance of a KNN classifier, namely CAR-based TDC method. The proposed classification ability function ranks a training sample in terms of its contribution to correctly classify other training samples as a KNN through the leave-one-out (LV1) strategy in the cleaning stage. The training sample that likely misclassifies the other samples during the KNN classifications according to the LV1 strategy is considered to have lower classification ability and will be cleaned out from the original training data set. Extensive experiments, based on ten real-world data sets, show that the proposed CAR-based TDC method can significantly reduce the classification error rates of KNN-based classifiers, while reducing computational complexity thanks to a smaller cleaned training data set.

Saikosaponin-d attenuated lipopolysaccharide-induced depressive-like behaviors via inhibiting microglia activation and neuroinflammation.

Saikosaponin-d (SSd), a triterpenoid saponins compound extracted from Radix Bupleuri, has been demonstrated to effectively alleviate chronic mild stress-induced depressive behaviors in rats, but the underlying molecular mechanisms are still uncertain. Increasing evidence indicated that microglia activation and inflammatory responses were involved in the pathogenesis of depression. Thus, we desired to induce inflammation-related depressive-like behaviors in mice by injecting lipopolysaccharide (LPS) to investigate whether the antidepressant effect of SSd is related to inhibiting inflammation. The results of behavioral tests showed that SSd administration ameliorated LPS-induced depressive-like behaviors, as shown by increased sucrose consumption in the sucrose preference test and decreased immobility time in the tail suspension test and forced swimming test. Furthermore, immunostaining results showed that SSd pretreatment inhibited LPS-induced microglia activation in the hippocampus of mice and primary microglia cells. Enzyme-linked immunosorbent assay (ELISA) results showed that SSd pretreatment suppressed LPS-induced overexpression of inflammatory factors such as interleukin (IL)-1ß, IL-6, tumor necrosis factor (TNF)-α both in vivo and in vitro. Immunostaining and western blot analysis results demonstrated that SSd pretreatment also inhibited LPS-induced HMGB1 translocation from nuclear to extracellular and decreased the protein levels of TLR4, p-IκB-α, NF-κBp65. These results suggested that SSd effectively improved LPS-induced inflammation-related depressive-like behaviors by inhibiting LPS-induced microglia activation and neuroinflammation, and the possible mechanism might associate with the regulation of the HMGB1/TLR4/NF-κB signaling pathway.

miR-451 suppresses bladder cancer cell migration and invasion via directly targeting c-Myc.

MicroRNA (miRNA) expression is shown dysregulated in tumors. It has been reported that miR-451 alters gene expression and regulates tumorigenesis in various cancer tissues. However, its underlying biological significance in bladder cancer remains to be clarified. In the present study, we investigated the function and molecular mechanism of miR-451 involved in bladder cancer cell migration and invasion. Our results showed that miR-451 was downregulated in clinical bladder carcinoma tissues compared with adjacent bladder tissues. Overexpression of miR-451 significantly retarded the proliferation, migration and invasion of bladder cancer T24 and 5637 cells in vitro. Moreover, the attenuated cell migration and invasion by miR-451 was correlated with increased apoptosis. However, our dual-luciferase reporter assay validated that c-Myc, an oncogene in many tumors, was a direct target gene of miR-451 in bladder cancer. The expression of c-Myc was repressed by miR-451 in bladder cancer cells, and knockdown of c-Myc mimicked the effects of miR-451 overexpression. This discovery suggested that miR-451 is a tumor suppressor modulating bladder cancer cell migration and invasion by directly targeting c-Myc. In addition, apoptosis promoted by miR-451 may participates in this biological behavior. Therefore, target miR-451 may be a novel therapeutic intervention for bladder cancer.