MultiFuseYOLO: Redefining Wine Grape Variety Recognition through Multisource Information Fusion.

Based on the current research on the wine grape variety recognition task, it has been found that traditional deep learning models relying only on a single feature (e.g., fruit or leaf) for classification can face great challenges, especially when there is a high degree of similarity between varieties. In order to effectively distinguish these similar varieties, this study proposes a multisource information fusion method, which is centered on the SynthDiscrim algorithm, aiming to achieve a more comprehensive and accurate wine grape variety recognition. First, this study optimizes and improves the YOLOV7 model and proposes a novel target detection and recognition model called WineYOLO-RAFusion, which significantly improves the fruit localization precision and recognition compared with YOLOV5, YOLOX, and YOLOV7, which are traditional deep learning models. Secondly, building upon the WineYOLO-RAFusion model, this study incorporated the method of multisource information fusion into the model, ultimately forming the MultiFuseYOLO model. Experiments demonstrated that MultiFuseYOLO significantly outperformed other commonly used models in terms of precision, recall, and F1 score, reaching 0.854, 0.815, and 0.833, respectively. Moreover, the method improved the precision of the hard to distinguish Chardonnay and Sauvignon Blanc varieties, which increased the precision from 0.512 to 0.813 for Chardonnay and from 0.533 to 0.775 for Sauvignon Blanc. In conclusion, the MultiFuseYOLO model offers a reliable and comprehensive solution to the task of wine grape variety identification, especially in terms of distinguishing visually similar varieties and realizing high-precision identifications.

Robust optical edge detection enabled by a twisted reflective q-plate.

Optical edge detection significantly reduces the image information load and is highly sought after in instant image processing. Robustness to the wavelength and polarization of light as well as mechanical vibration is a key requirement for practical applications. Here, a robust optical edge detector is proposed and demonstrated based on a reflective twisted liquid crystal q-plate. The device is composed of a mirror and a 1.46-µm-thick liquid crystal layer with a twist angle of 69.2°. The backtracking of the light inside the twisted medium forms a mirror symmetric twisted design and thus leads to a broadband self-compensated spiral phase modulation. By this means, an optical edge detector with excellent wavelength and polarization independence is presented for both coherent and partially coherent light sources. Additionally, the reflective design makes the system more compact and stable. This work supplies a practical design for robust optical edge detection, which may upgrade existing image processing techniques.

Chiral RuII -PtII Complexes Inducing Telomere Dysfunction against Cisplatin-Resistant Cancer Cells.

The application of G-quadruplex stabilizers presents a promising anticancer strategy. However, the molecular crowding conditions within cells diminish the potency of current G-quadruplex stabilizers. Herein, chiral RuII -PtII dinuclear complexes were developed as highly potent G-quadruplex stabilizers even under challenging molecular crowding conditions. The compounds were encapsulated with biotin-functionalized DNA cages to enhance sub-cellular localization and provide cancer selectivity. The nanoparticles were able to efficiently inhibit the endogenous activities of telomerase in cisplatin-resistant cancer cells and cause cell death by apoptosis. The nanomaterials demonstrated high antitumor activity towards cisplatin-resistant tumor cells as well as tumor-bearing mice. To the best of our knowledge, this study presents the first example of a RuII -PtII dinuclear complex as a G-quadruplex stabilizer with an anti-cancer effect towards drug-resistant tumors inside an animal model.

Supramolecular Assembly of An Organoplatinum(II) Complex with Ratiometric Dual Emission for Two-Photon Bioimaging.

The organoplatinum(II) complex [Pt(C^N^N)(Cl)] (C^N^N=5,6-diphenyl-2,2'-bipyridine, Pt1) can assemble into nanoaggregates via π-π stacking and complementary hydrogen bonds, rather than Pt-Pt interactions. Pt1 exhibits ratiometric dual emission, including rare blue emission (λem =445 nm) and assembly-induced yellow emission (λem =573 nm), under one- and two-photon excitation. Pt1 displays blue emission in cells with an intact membrane due to its low cellular uptake. In cells where the membrane is disrupted, uptake of the complex is increased and at higher concentrations yellow emission is observed. The ratio of yellow to blue emission shows a linear relationship to the loss of cell membrane integrity. Pt1 is, to our knowledge, the first example of an assembly-induced two-photon ratiometric dual emission organoplatinum complex. The excellent and unique characteristics of the complex enabled its use for the tracking of cell apoptosis, necrosis, and the inflammation process in zebrafish.

Biofriendly, Stretchable, and Reusable Hydrogel Electronics as Wearable Force Sensors.

The ever-growing overlap between stretchable electronic devices and wearable healthcare applications is igniting the discovery of novel biocompatible and skin-like materials for human-friendly stretchable electronics fabrication. Amongst all potential candidates, hydrogels with excellent biocompatibility and mechanical features close to human tissues are constituting a promising troop for realizing healthcare-oriented electronic functionalities. In this work, based on biocompatible and stretchable hydrogels, a simple paradigm to prototype stretchable electronics with an embedded three-dimensional (3D) helical conductive layout is proposed. Thanks to the 3D helical structure, the hydrogel electronics present satisfactory mechanical and electrical robustness under stretch. In addition, reusability of stretchable electronics is realized with the proposed scenario benefiting from the swelling property of hydrogel. Although losing water would induce structure shrinkage of the hydrogel network and further undermine the function of hydrogel in various applications, the worn-out hydrogel electronics can be reused by simply casting it in water. Through such a rehydration procedure, the dehydrated hydrogel can absorb water from the surrounding and then the hydrogel electronics can achieve resilience in mechanical stretchability and electronic functionality. Also, the ability to reflect pressure and strain changes has revealed the hydrogel electronics to be promising for advanced wearable sensing applications.

Chemical Exchange Saturation Transfer MR Imaging: Preliminary Results for Differentiation of Malignant and Benign Thoracic Lesions.

PURPOSE: To prospectively evaluate the capability of amide proton transfer-weighted chemical exchange saturation transfer magnetic resonance (MR) imaging for characterization of thoracic lesions. MATERIALS AND METHODS: The institutional review board approved this study, and written informed consent was obtained from 21 patients (13 men and eight women; mean age, 72 years) prior to enrollment. Each patient underwent chemical exchange saturation transfer MR imaging by using respiratory-synchronized half-Fourier fast spin-echo imaging after a series of magnetization transfer pulses. Next, a magnetization transfer ratio asymmetry at 3.5 ppm map was computationally generated. Pathology examinations resulted in a diagnosis of 13 malignant and eight benign thoracic lesions. The malignant lesions were further diagnosed as being nine lung cancers, comprising six adenocarcinomas, three squamous cell carcinomas, and four other thoracic malignancies. The Student t test was used to evaluate the capability of magnetization transfer ratio asymmetry (at 3.5 ppm), as assessed by means of region of interest measurements, for differentiating benign and malignant lesions, lung cancers and other thoracic lesions, and adenocarcinomas and squamous cell carcinomas. RESULTS: Magnetization transfer ratio asymmetry (at 3.5 ppm) was significantly higher for malignant tumors (mean ± standard deviation, 3.56% ± 3.01) than for benign lesions (0.33% ± 0.38, P = .008). It was also significantly higher for other thoracic malignancies (6.71% ± 3.46) than for lung cancer (2.16% ± 1.41, P = .005) and for adenocarcinoma (2.88% ± 1.13) than for squamous cell carcinoma (0.71% ± 0.17, P = .02). CONCLUSION: Amide proton transfer-weighted chemical exchange saturation transfer MR imaging allows characterization of thoracic lesions.

Lipophilic tetranuclear ruthenium(II) complexes as two-photon luminescent tracking non-viral gene vectors.

Fluorescence detection is the most effective tool for tracking gene delivery in living cells. To reduce photodamage and autofluorescence and to increase deep penetration into cells, choosing appropriate fluorophores that are capable of two-photon activation under irradiation in the NIR or IR regions is an effective approach. In this work, we have developed six tetranuclear ruthenium(II) complexes, GV1-6, and have studied their one- and two-photon luminescence properties. DNA interaction studies have demonstrated that GV2-6, bearing hydrophobic alkyl ether chains, show more efficient DNA condensing ability but lower DNA binding constants than GV1. However, the hydrophobic alkyl ether chains also enhance the DNA delivery ability of GV2-6 compared with that of GV1. More importantly, we have applied GV1-6 as non-viral gene vectors for tracking DNA delivery in living cells by one- and two-photon fluorescence microscopies. In two-photon microscopy, a high signal-to-noise contrast was achieved by irradiation with an 830 nm laser. This is the first example of the use of transition-metal complexes for two-photon luminescent tracking of the cellular pathways of gene delivery and as DNA carriers. Our work provides new insights into improving real-time tracking during gene delivery and transfection as well as important information for the design of multifunctional non-viral vectors.

Cyclometalated Iridium(III) Complexes as Two-Photon Phosphorescent Probes for Specific Mitochondrial Dynamics Tracking in Living Cells.

Five cyclometalated iridium(III) complexes with 2-phenylimidazo[4,5-f][1,10]phenanthroline derivatives (IrL1-IrL5) were synthesized and developed to image and track mitochondria in living cells under two-photon (750 nm) excitation, with two-photon absorption cross-sections of 48.8-65.5 GM at 750 nm. Confocal microscopy and inductive coupled plasma-mass spectrometry (ICP-MS) demonstrated that these complexes selectively accumulate in mitochondria within 5 min, without needing additional reagents for membrane permeabilization, or replacement of the culture medium. In addition, photobleaching experiments and luminescence measurements confirmed the photostability of these complexes under continuous laser irradiation and physiological pH resistance. Moreover, results using 3D multicellular spheroids demonstrate the proficiency of these two-photon luminescent complexes in deep penetration imaging. Two-photon excitation using such novel complexes of iridium(III) for exclusive visualization of mitochondria in living cells may substantially enhance practical applications of bioimaging and tracking.

Regional perfusion imaging using pTILT.

PURPOSE: To demonstrate the feasibility that the pseudo-continuous transfer insensitive labeling technique (pTILT) sequence can be modified to achieve regional perfusion imaging by either vessel-encoded or vessel-selective pTILT, similar to the regional perfusion imaging sequences using pseudo-continuous arterial spin labeling (pCASL). MATERIALS AND METHODS: Static phantom experiments were carried out to investigate the selectivity and efficiency of region-specific labeling by pTILT. The ability of the modified pTILT sequences to separate perfusion territories was also examined at two levels of the arterial tree on four healthy subjects: (i) the left and right internal carotid arteries (ICAs) and (ii) individual vascular territories of arteries above the circle of Willis (anterior cerebral artery [ACA], left and right middle cerebral arteries [MCAs]). RESULTS: In the phantom study, compared with non-region-specific pTILT, a labeling efficiency of 0.92 and 0.88 was measured at the target vessels using vessel-encoded and vessel-selective pTILT, respectively. In human studies, the perfusion territories of ACA, left and right MCA branches with estimated CBF of 74.5 ± 26.1, 65.9 ± 7.2, and 72.3 ± 14.5 mL/100 g/min, respectively, as well as more general left/right end territories (76.8 ± 12.7 and 72.8 ± 7.2 mL/100 g/min), were clearly delineated by the modified pTILT sequences for regional perfusion imaging. CONCLUSION: Two regional perfusion imaging modules (vessel-encoded and vessel-selective) used in pCASL can also be adapted into the pTILT sequence, and our results confirmed that the region-specific pTILT sequence is capable of regional perfusion imaging to separate perfusion territories of major cerebral arteries.

A novel PCDscore based on programmed cell death-related genes can effectively predict prognosis and therapy responses of colon adenocarcinoma.

Emerging evidence suggests a correlation between oncogenesis and programmed cell death (PCD). However, comprehensive studies that incorporate all identified PCD-related genes to guide colon adenocarcinoma (COAD) prognosis and precision treatment strategies are lacking. In this study, a series of bioinformatics analyses were comprehensively conducted using data from the TCGA-COAD, GSE17538, and GSE39582 cohorts. A total of 21 PCD-associated prognostic genes were identified through univariate Cox analysis. LASSO and multivariate Cox methods were employed to establish a prognostic gene signature (ALOX12, HSPA1A, IL13, MID2, RFFL, and SLC39A8) and the corresponding scoring system, termed PCDscore, which exhibited robust predictive ability. The ssGSEA and ESTIMATE algorithms were utilized to evaluate the tumor microenvironment of COAD. The high PCDscore group demonstrated a poorer prognosis, characterized by lower CD4+ T cell infiltration and a higher stromal score. In contrast, the low PCDscore group exhibited sensitivity to common chemotherapy drugs such as Cisplatin and 5-Fluorouracil. Single-cell sequencing analysis further revealed that the high-PCDscore group displayed a lower proportion of CD4+ T cells. Colorectal cancer samples from the years 2013-2017 were employed to validate the PCDscore, while those from 2018 to 2019 served as a temporal external validation set for the PCDscore. In vitro experimental results indicated that the overexpression of SLC39A8 inhibited the proliferation and invasion of colorectal cancer cells. The study developed a novel PCDscore system based on the analysis of genes related to all identified PCD types, providing valuable insights into clinical prognosis and drug sensitivity for patients with COAD.

Optimizing pTILT perfusion imaging in the presence of off-resonance frequency.

PURPOSE: To optimize the perfusion measurements and to recover loss of tagging efficiency from the blood flow measurements in the presence of static field inhomogeneity with the pseudo-continuous arterial spin labeling technique of pTILT, which is sensitive to off-resonance effects due to the employment of concatenated radiofrequency (RF) pulses for labeling. MATERIALS AND METHODS: Numerical Bloch simulations were performed to explore the labeling responses of concatenated RF pulses (45°, ±45°) in pTILT as a function of off-resonance frequency. A correction method was proposed by curve-fitting the measured blood flow signal to the signal model obtained from the simulations. The performance of the proposed correction method was examined for three healthy subjects on a 3 Tesla magnet after good shimming as well as under a "worst-case" scenario with deliberate mis-shimming. RESULTS: In cases after good shimming, in which the off-resonance frequency was less than 50 Hz, the perfusion measurements by the optimized pTILT sequence were improved by 12.7% compared with the original pTILT. With a "worst-case" mis-shimming and frequency offset by 80 Hz, the blood flow signal was increased by 68.9% and 64.8% for optimized global and localized pTILT, respectively. CONCLUSION: Addressing the impact of off-resonance frequency on concatenated RF pulses, tagging efficiency can be effectively recovered in pTILT perfusion measurements. This strategy may be extended to other applications of concatenated RF pulses where sensitivity to magnetic field inhomogeneity prevents accurate quantification.

Context Label Learning: Improving Background Class Representations in Semantic Segmentation.

Background samples provide key contextual information for segmenting regions of interest (ROIs). However, they always cover a diverse set of structures, causing difficulties for the segmentation model to learn good decision boundaries with high sensitivity and precision. The issue concerns the highly heterogeneous nature of the background class, resulting in multi-modal distributions. Empirically, we find that neural networks trained with heterogeneous background struggle to map the corresponding contextual samples to compact clusters in feature space. As a result, the distribution over background logit activations may shift across the decision boundary, leading to systematic over-segmentation across different datasets and tasks. In this study, we propose context label learning (CoLab) to improve the context representations by decomposing the background class into several subclasses. Specifically, we train an auxiliary network as a task generator, along with the primary segmentation model, to automatically generate context labels that positively affect the ROI segmentation accuracy. Extensive experiments are conducted on several challenging segmentation tasks and datasets. The results demonstrate that CoLab can guide the segmentation model to map the logits of background samples away from the decision boundary, resulting in significantly improved segmentation accuracy. Code is available at https://github.com/ZerojumpLine/CoLab.

Correlating the Microstructure and Current Density of the Li/Garnet Interface.

Solid-state lithium batteries hold great promise for next-generation energy storage systems. However, the formation of lithium filaments within the solid electrolyte remains a critical challenge. In this study, we investigate the crucial role of morphology in determining the resistance of garnet-type electrolytes to lithium filaments. By proposing a new test method, namely, cyclic linear sweep voltammetry, we can effectively evaluate the electrolyte resistance against lithium filaments. Our findings reveal a strong correlation between the microscopic morphology of the solid electrolyte and its resistance to lithium filaments. Samples with reduced pores and multiple grain boundaries demonstrate remarkable performance, achieving a critical current density of up to 3.2 mA cm-2 and excellent long-term cycling stability. Kelvin probe force microscopy and finite element method simulation results shed light on the impact of grain boundaries and electrolyte pores on lithium-ion transport and filament propagation. To inhibit lithium penetration, minimizing pores and achieving a uniform morphology with small grains and plenty of grain boundaries are essential.

Causality-Inspired Single-Source Domain Generalization for Medical Image Segmentation.

Deep learning models usually suffer from the domain shift issue, where models trained on one source domain do not generalize well to other unseen domains. In this work, we investigate the single-source domain generalization problem: training a deep network that is robust to unseen domains, under the condition that training data are only available from one source domain, which is common in medical imaging applications. We tackle this problem in the context of cross-domain medical image segmentation. In this scenario, domain shifts are mainly caused by different acquisition processes. We propose a simple causality-inspired data augmentation approach to expose a segmentation model to synthesized domain-shifted training examples. Specifically, 1) to make the deep model robust to discrepancies in image intensities and textures, we employ a family of randomly-weighted shallow networks. They augment training images using diverse appearance transformations. 2) Further we show that spurious correlations among objects in an image are detrimental to domain robustness. These correlations might be taken by the network as domain-specific clues for making predictions, and they may break on unseen domains. We remove these spurious correlations via causal intervention. This is achieved by resampling the appearances of potentially correlated objects independently. The proposed approach is validated on three cross-domain segmentation scenarios: cross-modality (CT-MRI) abdominal image segmentation, cross-sequence (bSSFP-LGE) cardiac MRI segmentation, and cross-site prostate MRI segmentation. The proposed approach yields consistent performance gains compared with competitive methods when tested on unseen domains.

Water-solubility croconic acid-bisindole dye with morpholine ring for tumor NIRF/PA imaging and photothermal therapy activated by lysosome pH-response.

A novel croconic acid-bisindole dye CR-630 with a morpholine ring showed good water-solubility and obvious lysosome-targeting. The protonation of the nitrogen atom in the indole and lysosome-targeting of morpholine ring let it exhibit stronger pH-responsive NIR/PA imaging and photothermal effect in the lysosome acidic microenvironment (pH 4.0-5.5) than in the tumor acidic microenvironment. In the animal study, compound CR-630 could NIRF/PA image in the tumor tissues in 1.5-2.0 h, effectively inhibit the growth of the tumor, and even ablate the tumor at the drug dose of 1 mg/kg. It also demonstrated good biosafety. This study gives a new idea to develop water-solubility organic dyes with lysosome targeting, stronger pH-responsive NIRF/PA imaging and PTT for breast cancer.

Localized blood flow imaging using quantitative flow-enhanced signal intensity.

Flow-enhanced signal intensity (FENSI) was previously introduced as a novel functional imaging method for measuring changes in localized blood flow in response to a stimulus. However, FENSI was limited to a qualitative functional MRI tool, due to magnetization transfer effects and different tagging plane profiles between tag and control images. In this work, a revised FENSI acquisition is proposed to enable quantitative imaging, which is capable of providing absolute localized blood flow maps free from magnetization transfer and slice profile errors. The feasibility and accuracy of measuring microvascular (arteriole, capillary, and venule) blood flow by using quantitative FENSI was validated by our phantom studies. Additionally, localized cerebral blood flow, 366 ± 45 µL/min/cm(2) in gray matter and 153 ± 23 µL/min/cm(2) in white matter, was measured in healthy subjects during resting state, whereas a flow change of 73 ± 13% was detected during a visual task.

Self-Supervised Learning for Few-Shot Medical Image Segmentation.

Fully-supervised deep learning segmentation models are inflexible when encountering new unseen semantic classes and their fine-tuning often requires significant amounts of annotated data. Few-shot semantic segmentation (FSS) aims to solve this inflexibility by learning to segment an arbitrary unseen semantically meaningful class by referring to only a few labeled examples, without involving fine-tuning. State-of-the-art FSS methods are typically designed for segmenting natural images and rely on abundant annotated data of training classes to learn image representations that generalize well to unseen testing classes. However, such a training mechanism is impractical in annotation-scarce medical imaging scenarios. To address this challenge, in this work, we propose a novel self-supervised FSS framework for medical images, named SSL-ALPNet, in order to bypass the requirement for annotations during training. The proposed method exploits superpixel-based pseudo-labels to provide supervision signals. In addition, we propose a simple yet effective adaptive local prototype pooling module which is plugged into the prototype networks to further boost segmentation accuracy. We demonstrate the general applicability of the proposed approach using three different tasks: organ segmentation of abdominal CT and MRI images respectively, and cardiac segmentation of MRI images. The proposed method yields higher Dice scores than conventional FSS methods which require manual annotations for training in our experiments.

Clinical evaluation of prone position ventilation in the treatment of acute respiratory distress syndrome induced by sepsis.

BACKGROUND: Acute respiratory distress syndrome (ARDS) is an acute, diffuse, inflammatory lung injury. Previous studies have shown prone position ventilation (PPV) to be associated with improvement in oxygenation. However, its role in patients with ARDS caused by sepsis remains unknown. AIM: To analyze the clinical effects of PPV in patients with ARDS caused by sepsis. METHODS: One hundred and two patients with ARDS were identified and divided into a control group (n = 55) and a PPV treatment group (n = 47). Outcomes included oxygenation index, lung compliance (Cst) and platform pressure (Pplat), which were compared between the two groups after ventilation. Other outcomes included heart rate (HR), mean arterial pressure (MAP), central venous pressure (CVP), left ventricular ejection fraction (LVEF), the length of mechanical ventilation time and intensive care unit (ICU) stay, and levels of C-reactive protein (CRP), procalcitonin (PCT), and interleukin-6 (IL-6) after ventilation. Finally, mortality rate was also compared between the two groups. RESULTS: On the first day after ventilation, the oxygenation index and Cst were higher and Pplat level was lower in the PPV group than in the conventional treatment group (P < 0.05). There were no significant differences in oxygenation index, Cst, and Pplat levels between the two groups on the 2nd, 4th, and 7th day after ventilation (P > 0.05). There were no significant differences in HR, MAP, CVP, LVEF, duration of mechanical ventilation and ICU stay, and the levels of CRP, PCT, and IL-6 between the two groups on the first day after ventilation (all P > 0.05). The mortality rates on days 28 and 90 in the PPV and control groups were 12.77% and 29.09%, and 25.53% and 45.45%, respectively (P < 0.05). CONCLUSION: PPV may improve respiratory mechanics indices and may also have mortality benefit in patients with ARDS caused by sepsis. Finally, PPV was not shown to cause any adverse effects on hemodynamics and inflammation indices.

Enhancing MR image segmentation with realistic adversarial data augmentation.

The success of neural networks on medical image segmentation tasks typically relies on large labeled datasets for model training. However, acquiring and manually labeling a large medical image set is resource-intensive, expensive, and sometimes impractical due to data sharing and privacy issues. To address this challenge, we propose AdvChain, a generic adversarial data augmentation framework, aiming at improving both the diversity and effectiveness of training data for medical image segmentation tasks. AdvChain augments data with dynamic data augmentation, generating randomly chained photo-metric and geometric transformations to resemble realistic yet challenging imaging variations to expand training data. By jointly optimizing the data augmentation model and a segmentation network during training, challenging examples are generated to enhance network generalizability for the downstream task. The proposed adversarial data augmentation does not rely on generative networks and can be used as a plug-in module in general segmentation networks. It is computationally efficient and applicable for both low-shot supervised and semi-supervised learning. We analyze and evaluate the method on two MR image segmentation tasks: cardiac segmentation and prostate segmentation with limited labeled data. Results show that the proposed approach can alleviate the need for labeled data while improving model generalization ability, indicating its practical value in medical imaging applications.

Cardiac segmentation on late gadolinium enhancement MRI: A benchmark study from multi-sequence cardiac MR segmentation challenge.

Accurate computing, analysis and modeling of the ventricles and myocardium from medical images are important, especially in the diagnosis and treatment management for patients suffering from myocardial infarction (MI). Late gadolinium enhancement (LGE) cardiac magnetic resonance (CMR) provides an important protocol to visualize MI. However, compared with the other sequences LGE CMR images with gold standard labels are particularly limited. This paper presents the selective results from the Multi-Sequence Cardiac MR (MS-CMR) Segmentation challenge, in conjunction with MICCAI 2019. The challenge offered a data set of paired MS-CMR images, including auxiliary CMR sequences as well as LGE CMR, from 45 patients who underwent cardiomyopathy. It was aimed to develop new algorithms, as well as benchmark existing ones for LGE CMR segmentation focusing on myocardial wall of the left ventricle and blood cavity of the two ventricles. In addition, the paired MS-CMR images could enable algorithms to combine the complementary information from the other sequences for the ventricle segmentation of LGE CMR. Nine representative works were selected for evaluation and comparisons, among which three methods are unsupervised domain adaptation (UDA) methods and the other six are supervised. The results showed that the average performance of the nine methods was comparable to the inter-observer variations. Particularly, the top-ranking algorithms from both the supervised and UDA methods could generate reliable and robust segmentation results. The success of these methods was mainly attributed to the inclusion of the auxiliary sequences from the MS-CMR images, which provide important label information for the training of deep neural networks. The challenge continues as an ongoing resource, and the gold standard segmentation as well as the MS-CMR images of both the training and test data are available upon registration via its homepage (www.sdspeople.fudan.edu.cn/zhuangxiahai/0/mscmrseg/).