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Clinician generated segmentation of tumor and healthy tissue regions of interest (ROIs) on medical images is crucial for radiotherapy. However, interobserver segmentation variability has long been considered a significant detriment to the implementation of high-quality and consistent radiotherapy dose delivery. This has prompted the increasing development of automated segmentation approaches. However, extant segmentation datasets typically only provide segmentations generated by a limited number of annotators with varying, and often unspecified, levels of expertise. In this data descriptor, numerous clinician annotators manually generated segmentations for ROIs on computed tomography images across a variety of cancer sites (breast, sarcoma, head and neck, gynecologic, gastrointestinal; one patient per cancer site) for the Contouring Collaborative for Consensus in Radiation Oncology challenge. In total, over 200 annotators (experts and non-experts) contributed using a standardized annotation platform (ProKnow). Subsequently, we converted Digital Imaging and Communications in Medicine data into Neuroimaging Informatics Technology Initiative format with standardized nomenclature for ease of use. In addition, we generated consensus segmentations for experts and non-experts using the Simultaneous Truth and Performance Level Estimation method. These standardized, structured, and easily accessible data are a valuable resource for systematically studying variability in segmentation applications.

To meet the requirements of high energy density and fast charge for energy storage systems and electric vehicles, the high-energy and high-power density lithium-ion batteries have attracted numerous attentions.Designing thick-electrode can significantly increase energy density and reduce cost, and is also compatible with various electrode materials, which makes it one of hottest researches for the development of high-energy density lithium-ion batteries.Thick electrodes usually suffer from poor mechanical properties and sluggish reaction kinetics. Therefore, it is very important to construct a thick electrode with good mechanical properties and fast transport network for lithium ion and electron.The electrochemical behavior and key scientific issues of thick electrodes were firstly analyzed in this review, the current strategies for constructing thick electrodes and their advantages were then introduced, and finally the design principles and the development direction of thick electrodes were pointed out.

Electrodes are vital for lithium‐ion battery performance. The primary method for large‐scale electrode production involves wet slurry casting methods, which encounter challenges related to solvent usage, energy consumption, and mechanical stability. Dry processed (DP) electrodes are a promising alternative but struggle with rate capability and mechanical properties. Here, an approach of incorporating 1 wt.% flour into DP electrodes (DP–1%F) through a binder fibrillation strategy is introduced, which enhances the mechanical strength, rate performance, and cycling stability of the electrodes. The cross‐linking of protein and starch in flour, along with the fibrillation of Polytetrafluoroethylene (PTFE), enable the DP–1%F electrode to exhibit robust mechanical properties and high flexibility. Additionally, the incorporation of flour makes the DP electrode primarily create large pores, reducing electrode tortuosity, thereby endowing the DP–1%F electrode with improved kinetic behavior. The robust mechanical properties and improved kinetic behavior suppress the development of irreversible phase transitions and intragranular/intergranular cracks. These characteristics led to the superior cycling stability of the DP–1%F electrodes with a capacity retention of 80.3% after 260 cycles at 2C and 4.5 V (LiNi0.8Co0.1Mn0.1O2). The findings offer valuable insights for the development of high‐power, long‐life DP electrodes, addressing key challenges in lithium‐ion batteries. [ABSTRACT FROM AUTHOR]

PURPOSE: The quality of radiotherapy auto-segmentation training data, primarily derived from clinician observers, is of utmost importance. However, the factors influencing the quality of clinician-derived segmentations are poorly understood; our study aims to quantify these factors. METHODS: Organ at risk (OAR) and tumor-related segmentations provided by radiation oncologists from the Contouring Collaborative for Consensus in Radiation Oncology data set were used. Segmentations were derived from five disease sites: breast, sarcoma, head and neck (H&N), gynecologic (GYN), and GI. Segmentation quality was determined on a structure-by-structure basis by comparing the observer segmentations with an expert-derived consensus, which served as a reference standard benchmark. The Dice similarity coefficient (DSC) was primarily used as a metric for the comparisons. DSC was stratified into binary groups on the basis of structure-specific expert-derived interobserver variability (IOV) cutoffs. Generalized linear mixed-effects models using Bayesian estimation were used to investigate the association between demographic variables and the binarized DSC for each disease site. Variables with a highest density interval excluding zero were considered to substantially affect the outcome measure. RESULTS: Five hundred seventy-four, 110, 452, 112, and 48 segmentations were used for the breast, sarcoma, H&N, GYN, and GI cases, respectively. The median percentage of segmentations that crossed the expert DSC IOV cutoff when stratified by structure type was 55% and 31% for OARs and tumors, respectively. Regression analysis revealed that the structure being tumor-related had a substantial negative impact on binarized DSC for the breast, sarcoma, H&N, and GI cases. There were no recurring relationships between segmentation quality and demographic variables across the cases, with most variables demonstrating large standard deviations. CONCLUSION: Our study highlights substantial uncertainty surrounding conventionally presumed factors influencing segmentation quality relative to benchmarks. Common radiation oncologist demographic factors are not associated with segmentation performance. [ABSTRACT FROM AUTHOR]