Perivascular spaces (PVS), when abnormally enlarged and visible in magnetic resonance imaging (MRI) structural sequences, are important imaging markers of cerebral small vessel disease and potential indicators of neurodegenerative conditions. Despite their clinical significance, automatic enlarged PVS (EPVS) segmentation remains challenging due to their small size, variable morphology, similarity with other pathological features, and limited annotated datasets. This paper presents the EPVS Challenge organized at MICCAI 2024, which aims to advance the development of automated algorithms for EPVS segmentation across multi-site data. We provided a diverse dataset comprising 100 training, 50 validation, and 50 testing scans collected from multiple international sites (UK, Singapore, and China) with varying MRI protocols and demographics. All annotations followed the STRIVE protocol to ensure standardized ground truth and covered the full brain parenchyma. Seven teams completed the full challenge, implementing various deep learning approaches primarily based on U-Net architectures with innovations in multi-modal processing, ensemble strategies, and transformer-based components. Performance was evaluated using dice similarity coefficient, absolute volume difference, recall, and precision metrics. The winning method employed MedNeXt architecture with a dual 2D/3D strategy for handling varying slice thicknesses. The top solutions showed relatively good performance on test data from seen datasets, but significant degradation of performance was observed on the previously unseen Shanghai cohort, highlighting cross-site generalization challenges due to domain shift. This challenge establishes an important benchmark for EPVS segmentation methods and underscores the need for the continued development of robust algorithms that can generalize in diverse clinical settings.

Accurate segmentation of brain tumor subregions from multi-modal MRI is critical for treatment planning but remains challenging due to heterogeneous lesion characteristics. Current deep learning approaches frequently underutilize modality-specific information, resulting in suboptimal segmentation accuracy for tumor subregions, and lack effective mechanisms to incorporate clinical diagnostic reasoning. To address these limitations, we propose a Clinical-Prompted Asymmetric Fusion Network (ClinPAF-Net). We introduce a dual-encoder architecture explicitly designed for multi-modal feature extraction, which preserves modality-specific features while enabling crossattention guided fusion. Specifically, we design an asymmetrybased bottleneck prompting mechanism that directs the attention of the model to symmetry-breaking regions by encoding pathological disruption of brain symmetry. Our method achieves state-of-the-art performance on BraTS 2020, improving mean Dice scores by 0.61% over prior arts. Ablation studies validate the effectiveness of each component, with the prompting mechanism boosting the segmentation of enhancing tumor by 0.8%. Our clinicallyguided approach demonstrates significant gains in segmentation precision and robustness for these challenging subregions.

Optical coherence tomography (OCT) has emerged as a clinically viable tool for detecting early cervical diseases due to its non-invasive, high-resolution imaging capabilities. While computer-aided OCT diagnosis systems have shown promising potential, they confront three primary challenges: limited annotated data, class imbalance, and cross-center bias. These factors collectively compromise model generalizability across medical centers. Therefore, we propose a joint structure-texture representation learning framework to enhance the generalization of cervical OCT diagnosis in multi-center, small-sample scenarios by leveraging histomorphological invariance. The framework synergizes hierarchical structure features from tissue segmentation with histomorphology-enhanced texture representations learned through lesion-focused contrastive reconstruction. We design a semi-supervised segmentation strategy that achieves promising segmentation performance for layered tissue architecture with minimal annotation burden. An attention-based feature fusion module dynamically combines complementary structural and textural features, generating enriched representations optimized for classification robustness. External cross-center validations demonstrate the superior generalization performance and interpretability of our approach over competitive baseline methods. Moreover, low model complexity and high inference efficiency make our approach well-suited for adoption in low-resource clinical settings.

Automatic multi-organ segmentation plays a critical role in medical image analysis, particularly in preoperative planning and disease assessment. However, conventional segmentation methods often struggle with challenges in handling small organs, ambiguous boundaries, and complex backgrounds, as well as in their generalization ability across different datasets. To address these limitations, we propose a novel model named IAMU-Net for multi-organ segmentation of abdominal computed tomography (CT) scans using deep learning. Our method is built upon the nnU-Net architecture and integrates multiple attention mechanisms to enhance feature representation, spatial information modeling, and multi-scale feature fusion abilities. Furthermore, we use a coarse-to-fine two-stage segmentation strategy to improve the segmentation accuracy of small organs and anatomically complex boundaries. Experimental results on the AbdomenAtlas 1.0 Mini and TotalSegmentator V2 datasets illustrate that our method achieves superior segmentation performance across multiple organs, confirming its effectiveness and robustness.

Cervical optical coherence tomography (OCT) is crucial in diagnosing cervical diseases due to its high-resolution imaging and non-invasive characteristics. However, recent improvements in classifying cervical OCT images have mainly focused on accuracy, often neglecting the importance of uncertainty and robustness in disease predictions. Uncertainty estimation faces challenges in collecting evidence for the minority classes, leading to fake confidence in evidential deep learning. There are similar issues with cervical OCT image classification. This study proposes a novel evidential learning process for cervical disease screening using OCT. It provides a measure of decision margin for each class with two novel mechanisms to address the hinge posted by class imbalance. Specifically, we introduce a reweighted margin adjustment strategy to learn less biased evidence among classes and make reliable uncertainty estimations. Extensive experiments on internal and external cervical OCT datasets demonstrate the proposed method outperforms competitive baseline methods, providing more reliable and robust predictions, particularly in out-of-distributions scenarios. By improving uncertainty estimation for class imbalance, our method offers valuable insights for practical clinical applications, highlighting the importance of measuring uncertainty in medical AI-aided diagnosis systems.

Cervical cancer is a significant global health concern for women. Optical coherence tomography (OCT) offers a non-invasive, high-resolution imaging method for cervical examinations. The clinical need for real-time AI-aided diagnosis in low-resource settings necessitates model compression for deep learning models. Therefore, we develop a novel activation map-based knowledge distillation (AMKD) framework to address this issue. The AMKD framework ensures that the compressed (student) model achieves classification performance approximating that of the teacher model while maintaining the same activation area, enhancing interpretability for gynecologists. Due to the lack of high-quality annotated data, we also utilize unlabeled images for self-supervised distillation pre-training to improve model performance. On an internal dataset, the compressed models based on ResNet, ConvNeXt, and Swin-Transformer outperformed existing knowledge distillation frameworks while demonstrating better interpretability. On two external validation sets, the best compressed (ResNet) model surpassed the average performance of four medical experts in sensitivity and negative predictive value for cervical OCT volume classification, showcasing enhanced lesion detection capabilities. For a cervical OCT image of 760 × 1200 pixels, the compressed ResNet model with 1.37 M parameters uses 1.13 GB GPU memory during inference and predicts the input image in 0.06 seconds, potentially improving real-time detection of cervical lesions in resource-limited clinical settings.

Pediatric teeth exhibit significant changes in type and spatial distribution across different age groups. This variation makes pediatric teeth segmentation from cone-beam computed tomography (CBCT) more challenging than that in adult teeth. Existing methods mainly focus on adult teeth segmentation, which however cannot be adapted to spatial distribution of pediatric teeth with individual changes (SDPTIC) in different children, resulting in limited accuracy for segmenting pediatric teeth. Therefore, we introduce a novel topology structure-guided graph convolutional network (TSG-GCN) to generate dynamic graph representation of SDPTIC for improved pediatric teeth segmentation. Specifically, this network combines a 3D GCN-based decoder for teeth segmentation and a 2D decoder for dynamic adjacency matrix learning (DAML) to capture SDPTIC information for individual graph representation. 3D teeth labels are transformed into specially-designed 2D projection labels, which is accomplished by first decoupling 3D teeth labels into class-wise volumes for different teeth via one-hot encoding and then projecting them to generate instance-wise 2D projections. With such 2D labels, DAML can be trained to adaptively describe SDPTIC from CBCT with dynamic adjacency matrix, which is then incorporated into GCN for improving segmentation. To ensure inter-task consistency at the adjacency matrix level between the two decoders, a novel loss function is designed. It can address the issue with inconsistent prediction and unstable TSG-GCN convergence due to two heterogeneous decoders. The TSG-GCN approach is finally validated with both public and multi-center datasets. Experimental results demonstrate its effectiveness for pediatric teeth segmentation, with significant improvement over seven state-of-the-art methods.

Optical coherence tomography (OCT) emerged as a promising, non-invasive, real-time, high-resolution imaging technology for cervical cancer detection. The scarcity of high-quality annotated cervical OCT images severely hinders the predictive performance of deep learning models. In contrast, self-supervised learning (SSL) methods offer a viable solution to the above challenge. This study aims to develop a cost-effective SSL framework that efficiently classifies high-resolution cervical OCT images to meet the clinical "see-and-diagnose" requirement for cervical lesions. Therefore, we propose COVE, a novel SSL framework with masked autoencoders based on VMamba with linear complexity. To our knowledge, COVE is the first SSL framework using masked image modeling for VMamba to leverage large unlabeled cervical OCT datasets. It has two specific designs: (1) 2D-Selective-Scan for visible patches (VP-SS2D): the VMamba encoder’s core module processes only visible patches for efficient pre-training and addressing the inconsistency between pre-training and fine-tuning; (2) visible patch feature-preserving (VPFP): visible patch features in each decoder block are replaced with encoder-extracted features, decoupling the encoder’s feature extraction from the decoder’s pixel reconstruction tasks. Experimental results showed that COVE outperformed existing SSL frameworks in five-fold cross-validation on a 1,452-subject cervical OCT dataset from a multi-center study and two external validation sets from top Chinese hospitals. Additionally, COVE demonstrated the highest pre-training efficiency, with significantly faster speed than existing SSL frameworks.

Cervical cancer is the fourth most common cancer among women worldwide, posing a severe threat to female reproductive health. The cervical optical coherence tomography (OCT) technology, known for its high-resolution imaging and non-invasive characteristics, holds significant potential in gynecology applications. Annotating cervical OCT images is based on pathological typing of cervical tissue from matching OCT images with the corresponding pathological slice images. It depends mostly on domain-specific skills and experience, lacking comprehensive, straightforward interpretation of subtypes for cervical OCT images. The accuracy of computer-aided diagnosis for cervical OCT images is always limited, failing to meet gynecologists’ clinical requirements. Therefore, our work aims to establish a fine-grained subtyping method for cervical OCT images to complement high-level pathological categories. The proposed method is implemented in a self-supervised pre-training style to explore OCT image characteristics fully. Unlike data-driven clustering methods, our method constructs a form of anchor points to leverage medical prior knowledge to avoid the mismatch between the discovered rule and existing pathological knowledge. We assessed our method with competitive baseline approaches on internal and external datasets. Besides, we collaborated with medical experts to demonstrate that the image feature patterns discovered are normative and clinically valuable for gynecologists.

Mobile edge computing (MEC) is an emerging computing paradigm receiving growing attention. MEC significantly reduces latency by processing user requests on edge servers rather than cloud centers, making it ideal for real-time applications. However, due to resource limitations and user mobility, microservice requests may fail, especially when users move at high speeds. This paper introduces a new tool, MobileEdgeSim, to simulate microservice-oriented MEC environments. MobileEdgeSim integrates mobility prediction and service composition to enhance the pre-deployment of microservices. To evaluate MobileEdgeSim, we conducted a series of experiments comparing it to several state-of-the-art baseline approaches. We also conducted a user study to evaluate the tool’s effectiveness in real-world scenarios. Our results indicate that MobileEdgeSim significantly improves the success rate of both user requests and responses while reducing resource costs. MobileEdgeSim is available at https://github.com/ssea-lab/MobileEdgesim.

With the widespread adoption of 5G and Internet of Things (IoT) technologies, the low latency provided by edge computing has great importance for real-time processing. However, managing numerous simultaneous service requests poses a significant challenge to maintaining low latency. Current edge server task scheduling methods often fail to balance multiple optimization goals effectively. This paper introduces a novel task-scheduling approach based on Evolutionary Computing (EC) theory and heuristic algorithms. We model service requests as task sequences and evaluate various scheduling schemes during each evolutionary process using Large Language Models (LLMs) services. Experimental results show that our task-scheduling algorithm outperforms existing heuristic and traditional reinforcement learning methods. Additionally, we investigate the effects of different heuristic strategies and compare the evolutionary outcomes across various LLM services.

Fetal Magnetic Resonance Imaging (MRI) is challenged by fetal movements and maternal breathing. Although fast MRI sequences allow artifact free acquisition of individual 2D slices, motion frequently occurs in the acquisition of spatially adjacent slices. Motion correction for each slice is thus critical for the reconstruction of 3D fetal brain MRI. In this paper, we propose a novel multi-task learning framework that adopts a coarse-to-fine strategy to jointly learn the pose estimation parameters for motion correction and tissue segmentation map of each slice in fetal MRI. Particularly, we design a regression-based segmentation loss as a deep supervision to learn anatomically more meaningful features for pose estimation and segmentation. In the coarse stage, a U-Net-like network learns the features shared for both tasks. In the refinement stage, to fully utilize the anatomical information, signed distance maps constructed from the coarse segmentation are introduced to guide the feature learning for both tasks. Finally, iterative incorporation of the signed distance maps further improves the performance of both regression and segmentation progressively. Experimental results of cross-validation across two different fetal datasets acquired with different scanners and imaging protocols demonstrate the effectiveness of the proposed method in reducing the pose estimation error and obtaining superior tissue segmentation results simultaneously, compared with state-of-the-art methods.

In-utero fetal MRI is emerging as an important tool in the diagnosis and analysis of the developing human brain. Automatic segmentation of the developing fetal brain is a vital step in the quantitative analysis of prenatal neurodevelopment both in the research and clinical context. However, manual segmentation of cerebral structures is time-consuming and prone to error and inter-observer variability. Therefore, we organized the Fetal Tissue Annotation (FeTA) Challenge in 2021 in order to encourage the development of automatic segmentation algorithms on an international level. The challenge utilized FeTA Dataset, an open dataset of fetal brain MRI reconstructions segmented into seven different tissues (external cerebrospinal fluid, gray matter, white matter, ventricles, cerebellum, brainstem, deep gray matter). 20 international teams participated in this challenge, submitting a total of 21 algorithms for evaluation. In this paper, we provide a detailed analysis of the results from both a technical and clinical perspective. All participants relied on deep learning methods, mainly U-Nets, with some variability present in the network architecture, optimization, and image pre- and post-processing. The majority of teams used existing medical imaging deep learning frameworks. The main differences between the submissions were the fine tuning done during training, and the specific pre- and post-processing steps performed. The challenge results showed that almost all submissions performed similarly. Four of the top five teams used ensemble learning methods. However, one team's algorithm performed significantly superior to the other submissions, and consisted of an asymmetrical U-Net network architecture. This paper provides a first of its kind benchmark for future automatic multi-tissue segmentation algorithms for the developing human brain in utero.

Total anomalous pulmonary venous connection (TAPVC) is a rare but mortal congenital heart disease in children and can be repaired by surgical operations. However, some patients may suffer from pulmonary venous obstruction (PVO) after surgery with insufficient blood supply, necessitating special follow-up strategy and treatment. Therefore, it is a clinically important yet challenging problem to predict such patients before surgery. In this paper, we address this issue and propose a computational framework to determine the risk factors for postoperative PVO (PPVO) from computed tomography angiography (CTA) images and build the PPVO risk prediction model. From clinical experiences, such risk factors are likely from the left atrium (LA) and pulmonary vein (PV) of the patient. Thus, 3D models of LA and PV are first reconstructed from low-dose CTA images. Then, a feature pool is built by computing different morphological features from 3D models of LA and PV, and the coupling spatial features of LA and PV. Finally, four risk factors are identified from the feature pool using the machine learning techniques, followed by a risk prediction model. As a result, not only PPVO patients can be effectively predicted but also qualitative risk factors reported in the literature can now be quantified. Finally, the risk prediction model is evaluated on two independent clinical datasets from two hospitals. The model can achieve the AUC values of 0.88 and 0.87 respectively, demonstrating its effectiveness in risk prediction.

The competitive endogenous RNA (ceRNA) hypothesis, which suggests a global regulatory mechanism operating through miRNA-mediated cross talks, has met with considerable skepticism. Quantitative analyses demonstrate that the absolute abundance of highly expressed miRNAs typically far exceeds that of sponging mRNAs, hence ceRNA-based regulation is unlikely under physiological conditions. However, whether the highly expressed miRNAs dominate the global ceRNA network or not has not been analyzed. Here we systematically examined miRNAs and miRNA-binding mRNAs across a large cohort of tissues. Intriguingly, while a few highly-expressed miRNAs (hi-miRs) dominate the miRNA landscape in all analyzed tissues, there are many more miRNAs expressed at intermediate levels (med-miRs), and ceRNA interactions are possible among med-miR-associated mRNAs. Critically, the number of med-miR-associated mRNAs is substantially higher than hi-miR-associated mRNAs. Consequently, the med-miR-mediated ceRNA network is not significantly inhibited by hi-miRs and are operational despite the presence of highly expressed miRNAs. Taken together, our analyses demonstrated that functional ceRNA networks are evolved to perform distinct functions from hi-miRs, and are likely of physiological significance.