Medical image segmentation models struggle with rare abnormalities due to scarce annotated pathological data. We propose DiffAug a novel framework that combines textguided diffusion-based generation with automatic segmentation validation to address this challenge. Our proposed approach uses latent diffusion models conditioned on medical text descriptions and spatial masks to synthesize abnormalities via inpainting on normal images. Generated samples undergo dynamic quality validation through a latentspace segmentation network that ensures accurate localization while enabling single-step inference. The text prompts, derived from medical literature, guide the generation of diverse abnormality types without requiring manual annotation. Our validation mechanism filters synthetic samples based on spatial accuracy, maintaining quality while operating efficiently through direct latent estimation. Evaluated on three medical imaging benchmarks (CVC-ClinicDB, Kvasir-SEG, REFUGE2), our framework achieves state-of-the-art performance with 8-10% Dice improvements over baselines and reduces false negative rates by up to 28% for challenging cases like small polyps and flat lesions critical for early detection in screening applications.

Accurate and automated segmentation of 3D biomedical images is a sophisticated imperative in clinical diagnosis, imaging-guided surgery, and prognosis judgment. Although the burgeoning of deep learning technologies has fostered smart segmentators, the successive and simultaneous garnering global and local features still remains challenging, which is essential for an exact and efficient imageological assay. To this end, a segmentation solution dubbed the mixed parallel shunted transformer (MPSTrans) is developed here, highlighting 3D-MPST blocks in a U-form framework. It enabled not only comprehensive characteristic capture and multiscale slice synchronization but also deep supervision in the decoder to facilitate the fetching of hierarchical representations. Performing on an unpublished colon cancer data set, this model achieved an impressive increase in dice similarity coefficient (DSC) and a 1.718 mm decease in Hausdorff distance at 95% (HD95), alongside a substantial shrink of computational load of 56.7% in giga floating-point operations per second (GFLOPs). Meanwhile, MPSTrans outperforms other mainstream methods (Swin UNETR, UNETR, nnU-Net, PHTrans, and 3D U-Net) on three public multiorgan (aorta, gallbladder, kidney, liver, pancreas, spleen, stomach, etc.) and multimodal (CT, PET-CT, and MRI) data sets of medical segmentation decathlon (MSD) brain tumor, multiatlas labeling beyond cranial vault (BCV), and automated cardiac diagnosis challenge (ACDC), accentuating its adaptability. These results reflect the potential of MPSTrans to advance the state-of-the-art in biomedical imaging analysis, which would offer a robust tool for enhanced diagnostic capacity.

During labor, transperineal ultrasound imaging can acquire real-time midsagittal images, through which the pubic symphysis and fetal head can be accurately identified, and the angle of progression (AoP) between them can be calculated, thereby quantitatively evaluating the descent and position of the fetal head in the birth canal in real time. However, current segmentation methods based on convolutional neural networks (CNNs) and Transformers generally depend heavily on large-scale manually annotated data, which limits their adoption in practical applications. In light of this limitation, this paper develops a new Transformer-based Semi-supervised Segmentation Network (TransSeg). This method employs a Vision Transformer as the backbone network and introduces a Channel-wise Cross Attention (CCA) mechanism to effectively reconstruct the features of unlabeled samples into the labeled feature space, promoting architectural innovation in semi-supervised segmentation and eliminating the need for complex training strategies. In addition, we design a Semantic Information Storage (S-InfoStore) module and a Channel Semantic Update (CSU) strategy to dynamically store and update feature representations of unlabeled samples, thereby continuously enhancing their expressiveness in the feature space and significantly improving the model's utilization of unlabeled data. We conduct a systematic evaluation of the proposed method on the FH-PS-AoP dataset. Experimental results demonstrate that TransSeg outperforms existing mainstream methods across all evaluation metrics, verifying its effectiveness and advancement in semi-supervised semantic segmentation tasks.

Functional hyperaemia is a well-established hallmark of healthy brain function, whereby local brain blood flow adjusts in response to a change in the activity of the surrounding neurons. Although functional hyperemia has been extensively studied at the level of both tissue and individual vessels, vascular network-level coordination remains largely unknown. To bridge this gap, we developed a deep learning-based computational pipeline that uses two-photon fluorescence microscopy images of cerebral microcirculation to enable automated reconstruction and quantification of the geometric changes across the microvascular network, comprising hundreds of interconnected blood vessels, pre and post-activation of the neighbouring neurons. The pipeline's utility was demonstrated in the Thy1-ChR2 optogenetic mouse model, where we observed network-wide vessel radius changes to depend on the photostimulation intensity, with both dilations and constrictions occurring across the cortical depth, at an average of 16.1{+/-}14.3 m (mean{+/-}stddev) away from the most proximal neuron for dilations; and at 21.9{+/-}14.6 m away for constrictions. We observed a significant heterogeneity of the vascular radius changes within vessels, with radius adjustment varying by an average of 24 {+/-} 28% of the resting diameter, likely reflecting the heterogeneity of the distribution of contractile cells on the vessel walls. A graph theory-based network analysis revealed that the assortativity of adjacent blood vessel responses rose by 152 {+/-} 65% at 4.3 mW/mm2 of blue photostimulation vs. the control, with a 4% median increase in the efficiency of the capillary networks during this level of blue photostimulation in relation to the baseline. Interrogating individual vessels is thus not sufficient to predict how the blood flow is modulated in the network. Our computational pipeline, to be made openly available, enables tracking of the microvascular network geometry over time, relating caliber adjustments to vessel wall-associated cells' state, and mapping network-level flow distribution impairments in experimental models of disease.

The process of detecting and measuring the fat layer surrounding the heart from medical images is referred to as epicardial fat segmentation. Accurate segmentation is essential for assessing heart health and associated risk factors. It plays a critical role in evaluating cardiovascular disease, requiring advanced techniques to enhance precision and effectiveness. However, there is currently a shortage of resources made for fat mass measurement. The Visual Lab's cardiac fat database addresses this limitation by providing a comprehensive set of high-resolution images crucial for reliable fat analysis. This study proposes a novel method for epicardial fat segmentation, involving a multi-stage framework. In the preprocessing phase, window-aware guided bilateral filtering (WGBR) is applied to reduce noise while preserving structural features. For region-of-interest (ROI) selection, the White Shark Optimizer (WSO) is employed to improve exploration and exploitation accuracy. The segmentation task is handled using a bidirectional guided semi-3D network (BGSNet), which enhances robustness by extracting features in both forward and backward directions. Following segmentation, quantification is performed to estimate the epicardial fat volume. This is achieved using reflection-equivariant quantum neural networks (REQNN), which are well-suited for modelling complex visual patterns. The Parrot optimizer is further utilized to fine-tune hyperparameters, ensuring optimal performance. The experimental results confirm the effectiveness of the suggested BGSNet with REQNN approach, achieving a Dice score of 99.50 %, an accuracy of 99.50 %, and an execution time of 1.022 s per slice. Furthermore, the Spearman correlation coefficient for fat quantification yielded an R² value of 0.9867, indicating a strong agreement with the reference measurements. This integrated approach offers a reliable solution for epicardial fat segmentation and quantification, thereby supporting improved cardiovascular risk assessment and monitoring.

Polyp shape is critical for diagnosing colorectal polyps and assessing cancer risk, yet there is limited data on segmenting pedunculated and sessile polyps. This paper introduces PolypDB_INS, a dataset of 4403 images containing 4918 annotated polyps, specifically for sessile and pedunculated polyps. In addition, we propose DYNAFormer, a novel transformer-based model utilizing an anchor mask-guided mechanism that incorporates cross-attention, dynamic query updates, and query denoising for improved object segmentation. Treating each positional query as an anchor mask dynamically updated through decoder layers enhances perceptual information regarding the object's position, allowing for more precise segmentation of complex structures like polyps. Extensive experiments on the PolypDB_INS dataset using standard evaluation metrics for both instance and semantic segmentation show that DYNAFormer significantly outperforms state-of-the-art methods. Ablation studies confirm the effectiveness of the proposed techniques, highlighting the model's robustness for diagnosing colorectal cancer. The source code and dataset are available at https://github.com/ntcongvn/DYNAFormer https://github.com/ntcongvn/DYNAFormer.

Large annotated datasets are vital for training segmentation models, but pixel-level labeling is time-consuming, error-prone, and often requires scarce expert annotators, especially in medical imaging. In contrast, coarse annotations are quicker, cheaper, and easier to produce, even by non-experts. In this paper, we propose to use coarse drawings from both positive (target) and negative (background) classes in the image, even with noisy pixels, to train a convolutional neural network (CNN) for semantic segmentation. We present a method for learning the true segmentation label distributions from purely noisy coarse annotations using two coupled CNNs. The separation of the two CNNs is achieved by high fidelity with the characters of the noisy training annotations. We propose to add a complementary label learning that encourages estimating negative label distribution. To illustrate the properties of our method, we first use a toy segmentation dataset based on MNIST. We then present the quantitative results of experiments using publicly available datasets: Cityscapes dataset for multi-class segmentation, and retinal images for medical applications. In all experiments, our method outperforms state-of-the-art methods, particularly in the cases where the ratio of coarse annotations is small compared to the given dense annotations.

Glomeruli are crucial for blood filtration, waste removal, and regulation of essential substances in the body. Traditional methods for detecting glomeruli rely on human interpretation, which can lead to variability. AI techniques have improved this process; however, most studies have used images with fixed magnification. This study proposes a novel magnification-integrated ensemble method to enhance glomerular segmentation accuracy. Whole-slide images (WSIs) from 12 patients were used for training, two for validation, and one for testing. Patch and mask images were extracted at 256 × 256 size × x2, x3, and x4 magnification levels. Data augmentation techniques, such as RandomResize, RandomCrop, and RandomFlip, were used. The segmentation model underwent 80,000 iterations with a stochastic gradient descent (SGD). Performance varied with changes in magnification. The models trained on high-magnification images showed significant drops when tested at lower magnifications, and vice versa. The proposed method improved segmentation accuracy across different magnifications, achieving 87.72 mIoU and 93.04 Dice score with the U-Net model. The magnification-integrated ensemble method significantly enhanced glomeruli segmentation accuracy across varying magnifications, thereby addressing the limitations of fixed magnification models. This approach improves the robustness and reliability of AI-driven diagnostic tools, potentially benefiting various medical imaging applications by ensuring consistent performance despite changes in image magnification.

Sarcopenia is a progressive loss of muscle mass and function linked to poor surgical outcomes such as prolonged hospital stays, impaired mobility, and increased mortality. Although it can be assessed through cross-sectional imaging by measuring skeletal muscle area (SMA), the process is time-consuming and adds to clinical workloads, limiting timely detection and management; however, this process could become more efficient and scalable with the assistance of artificial intelligence applications. This paper presents high-quality three-dimensional cross-sectional computed tomography (CT) images of patients with sarcopenia collected at the Freeman Hospital, Newcastle upon Tyne Hospitals NHS Foundation Trust. Expert clinicians manually annotated the SMA at the third lumbar vertebra, generating precise segmentation masks. We develop deep-learning models to measure SMA in CT images and automate this task. Our methodology employed transfer learning and self-supervised learning approaches using labelled and unlabeled CT scan datasets. While we developed qualitative assessment models for detecting sarcopenia, we observed that the quantitative assessment of SMA is more precise and informative. This approach also mitigates the issue of class imbalance and limited data availability. Our model predicted the SMA, on average, with an error of +-3 percentage points against the manually measured SMA. The average dice similarity coefficient of the predicted masks was 93%. Our results, therefore, show a pathway to full automation of sarcopenia assessment and detection.

To evaluate organs-at-risk (OARs) segmentation variability across eight commercial AI-based segmentation software using independent multi-institutional datasets, and to provide recommendations for clinical practices utilizing AI-segmentation. 160 planning CT image sets from four anatomical sites: head-and-neck, thorax, abdomen and pelvis were retrospectively pooled from three institutions. Contours for 31 OARs generated by the software were compared to clinical contours using multiple accuracy metrics, including: Dice similarity coefficient (DSC), 95 Percentile of Hausdorff distance (HD95), surface DSC, as well as relative added path length (RAPL) as an efficiency metric. A two-factor analysis of variance was used to quantify variability in contouring accuracy across software platforms (inter-software) and patients (inter-patient). Pairwise comparisons were performed to categorize the software into different performance groups, and inter-software variations (ISV) were calculated as the average performance differences between the groups. Significant inter-software and inter-patient contouring accuracy variations (p<0.05) were observed for most OARs. The largest ISV in DSC in each anatomical region were cervical esophagus (0.41), trachea (0.10), spinal cord (0.13) and prostate (0.17). Among the organs evaluated, 7 had mean DSC >0.9 (i.e., heart, liver), 15 had DSC ranging from 0.7 to 0.89 (i.e., parotid, esophagus). The remaining organs (i.e., optic nerves, seminal vesicle) had DSC<0.7. 16 of the 31 organs (52%) had RAPL less than 0.1. Our results reveal significant inter-software and inter-patient variability in the performance of AI-segmentation software. These findings highlight the need of thorough software commissioning, testing, and quality assurance across disease sites, patient-specific anatomies and image acquisition protocols.