The human red nucleus and lateral cerebellum in supporting roles for sensory information processing

A functional MRI study compared activation in the red nucleus to that in the lateral cerebellar dentate nucleus during passive and active tactile discrimination tasks. The study pursued recent neuroimaging results suggesting that the cerebellum may be more associated with sensory processing than with the control of movement for its own sake. Because the red nucleus interacts closely with the cerebellum, the possibility was examined that activity in red nucleus might also be driven by the requirement for tactile sensory processing with the fingers rather than by finger movement alone. The red and dentate nuclei were about 300% more active (a combination of activation areas and intensities) during passive (non‐motor) tactile stimulation when discrimination was required than when it was not. Thus, the red nucleus was activated by purely sensory stimuli even in the absence of the opportunity to coordinate finger movements or to use the sensory cues to guide movement. The red and dentate nuclei were about 70% more active during active tactile tasks when discrimination was required than when it was not (i.e., for simple finger movements alone). Thus, the red nucleus was most active when the fingers were being used for tactile sensory discrimination. In both the passive and active tactile tasks, the observed activation had a contralateralized pattern, with stronger activation in the left red nucleus and right dentate nucleus. Significant covariation was observed between activity in the red nucleus and the contralateral dentate during the discrimination tasks and no significant correlation between the red nucleus and the contralateral dentate activity was detected during the two non‐discrimination tasks. The observed interregional covariance and contralateralized activation patterns suggest strong functional connectivity during tactile discrimination tasks. Overall, the pattern of findings suggests that the activity in the red nucleus, as in the lateral cerebellum, is more driven by the requirements for sensory processing than by motor coordination per se

Computer‐assisted Curie scoring for metaiodobenzylguanidine (MIBG) scans in patients with neuroblastoma

BackgroundRadiolabeled metaiodobenzylguanidine (MIBG) is sensitive and specific for detecting neuroblastoma. The extent of MIBG‐avid disease is assessed using Curie scores. Although Curie scoring is prognostic in patients with high‐risk neuroblastoma, there is no standardized method to assess the response of specific sites of disease over time. The goal of this study was to develop approaches for Curie scoring to facilitate the calculation of scores and comparison of specific sites on serial scans.ProcedureWe designed three semiautomated methods for determining Curie scores, each with increasing degrees of computer assistance. Method A was based on visual assessment and tallying of MIBG‐avid lesions. For method B, scores were tabulated from a schematic that associated anatomic regions to MIBG‐positive lesions. For method C, an anatomic mesh was used to mark MIBG‐positive lesions with automatic assignment and tallying of scores. Five imaging physicians experienced in MIBG interpretation scored 38 scans using each method, and the feasibility and utility of the methods were assessed using surveys.ResultsThere was good reliability between methods and observers. The user‐interface methods required 57 to 110 seconds longer than the visual method. Imaging physicians indicated that it was useful that methods B and C enabled tracking of lesions. Imaging physicians preferred method B to method C because of its efficiency.ConclusionsWe demonstrate the feasibility of semiautomated approaches for Curie score calculation. Although more time was needed for strategies B and C, the ability to track and document individual MIBG‐positive lesions over time is a strength of these methods.Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/146464/1/pbc27417.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/146464/2/pbc27417_am.pd

Flink-ER: An Elastic Resource-Scheduling Strategy for Processing Fluctuating Mobile Stream Data on Flink

As real-time and immediate feedback becomes increasingly important in tasks related to mobile information, big data stream processing systems are increasingly applied to process massive amounts of mobile data. However, when processing a drastically fluctuating mobile data stream, the lack of an elastic resource-scheduling strategy limits the elasticity and scalability of data stream processing systems. To address this problem, this paper builds a flow-network model, a resource allocation model, and a data redistribution model as the foundation for proposing Flink with an elastic resource-scheduling strategy (Flink-ER), which consists of a capacity detection algorithm, an elastic resource reallocation algorithm, and a data redistribution algorithm. The strategy improves the performance of the platform by dynamically rescaling the cluster and increasing the parallelism of operators based on the processing load. The experimental results show that the throughput of a cluster was promoted under the premise of meeting latency constraints, which verifies the efficiency of the strategy

Whole-body tumor segmentation from PET/CT images using a two-stage cascaded neural network with camouflaged object detection mechanisms

Background: Whole-body Metabolic Tumor Volume (MTVwb) is an independent prognostic factor for overall survival in lung cancer patients. Automatic segmentation methods have been proposed for MTV calculation. Nevertheless, most of existing methods for patients with lung cancer only segment tumors in the thoracic region. Purpose: In this paper, we present a Two-Stage cascaded neural network integrated with Camouflaged Object Detection mEchanisms (TS-Code-Net) for automatic segmenting tumors from whole-body PET/CT images. Methods: Firstly, tumors are detected from the Maximum Intensity Projection (MIP) images of PET/CT scans, and tumors' approximate localizations along z-axis are identified. Secondly, the segmentations are performed on PET/CT slices that contain tumors identified by the first step. Camouflaged object detection mechanisms are utilized to distinguish the tumors from their surrounding regions that have similar Standard Uptake Values (SUV) and texture appearance. Finally, the TS-Code-Net is trained by minimizing the total loss that incorporates the segmentation accuracy loss and the class imbalance loss. Results: The performance of the TS-Code-Net is tested on a whole-body PET/CT image data-set including 480 Non-Small Cell Lung Cancer (NSCLC) patients with five-fold cross-validation using image segmentation metrics. Our method achieves 0.70, 0.76, and 0.70, for Dice, Sensitivity and Precision, respectively, which demonstrates the superiority of the TS-Code-Net over several existing methods related to metastatic lung cancer segmentation from whole-body PET/CT images. Conclusions: The proposed TS-Code-Net is effective for whole-body tumor segmentation of PET/CT images. Codes for TS-Code-Net are available at: https://github.com/zyj19/TS-Code-Net.</p