DynU-Net: Dynamic Uncertainty-Aware Multi-task U-Net for Joint Lesion Segmentation and Classification in Medical Imaging

MRD-UNet: a compute-efficient medical image segmentation architecture with multi-attention and parallel dilated fusion

Medical image segmentation plays a critical role in computer-aided diagnosis by enabling precise delineation of abnormalities in medical images. Accurate segmentation of these images is essential for early diagnosis and treatment planning. However, conventional models such as U-Net face challenges including boundary uncertainty, variations in image quality and limited generalization across datasets. This study presents MRD-UNet, a U-Net based segmentation framework built on a channel-disjoint multi-path dilated fusion (MDF) block deployed uniformly across all encoder and decoder stages, a residual formulation within each MDF block that stabilizes gradient flow without introducing pooling branches, and a task-driven distribution of three functionally distinct attention modules. We evaluate the proposed model through extensive experiments on the CVC-ClinicDB dataset comprising 612 endoscopic images and the ISIC-2018 dataset containing 2,594 dermoscopic images. During data augmentation, we apply geometric transformations, elastic deformations, and brightness adjustments; training is performed using a composite loss that combines Dice loss and binary cross-entropy. The proposed method achieves a DSC of 0.9140 on CVC-ClinicDB and 0.9106 on ISIC-2018, yielding an improvement of approximately 2–3% over the baseline U-Net architecture. Ablation studies confirm that the channel-disjoint MDF design reduces the number of parameters relative to the baseline while improving sensitivity to small and low-contrast lesions, and that the targeted attention layout enhances boundary delineation. MRD-UNet provides a practical balance between segmentation accuracy and computational efficiency. It outperforms baseline CNNs and performs comparably to heavier transformer-based models while using significantly fewer parameters. Cross-dataset evaluations demonstrate its ability to generalize within the same imaging modality. Notably, the lightweight MRD-UNet-S variant maintains competitive accuracy, showing the architecture’s suitability for resource-constrained medical image segmentation tasks. Not applicable.

DSGH-Net: Medical Image Segmentation via Dual-Statistical Dynamic Context and Graph-Convolutional Heterogeneous Decoder.

Automated 3D Brain Tumor Segmentation using Attention U-Net with Multi-Task Regularization on Multiparametric MRI

In order to make accurate distinctions within gliomas - specifically necrosis, edema, and enhancement - accurate segmentation of these regions from multiple MRI types is required. This task has proven difficult because of how heavily class imbalance affects voxels in this imaging modality, combined with its high degree of morphologic variability. In response to this challenge, a new compact 3D attention-based U-Net is developed that uses multi-task regularization, which is used for the volumetric segmentation of gliomas using data from the BraTS 2023 competition. By applying attention gates at each skip connection in the proposed model, non-tumor-related activations are suppressed before the output of the decoder is concatenated together. Additionally, the addition of a lightweight binary auxiliary classification layer after the first bottleneck in the model can act as an inductive bias to prevent the encoder from collapsing into shortcut texture features based solely on local information when faced with severe class imbalance. The model was tested on a held-out set of 251 patients, resulting in Dice similarity coefficients of 0.927 (whole tumor), 0.877 (tumor core), and 0.831 (enhancing tumor). Furthermore, the median HD95 found for whole tumor boundary localization was 1.57 mm. The model has approximately 5.64 million trainable parameters and performs inference in approximately 0.18 s per patient on a single GPU. This represents a 70% decrease in the number of trainable parameters compared to standard 3D U-Net architectures performing similarly well on segmentation tasks, thus making it suitable for use in resource-constrained deployment settings.

Semi-supervised Medical Image Segmentation via Perturbation-Aware Mutual Learning and Edge-Aware Uncertainty Loss for Accurate Anatomical Delineation.

This paper proposes a novel framework that effectively leverages unlabeled data to improve segmentation performance in cardiac structures and applies a novel consistency constraint by a dual fine-grained boundary loss that provide global characteristics-based guidance from the transition of the boundary region and an edge-aware uncertainty loss.

REC-UNet: a 2D U-Net model with residual cross-dimensional attention for liver tumor segmentation

Liver tumors impose a significant global health burden, underscoring the urgent need for efficient and accurate diagnostic methods. Computer-assisted techniques, particularly deep learning-based segmentation models, have shown considerable promise in this domain. However, they continue to face persistent challenges in liver tumor segmentation, including severe background noise interference, indistinct lesion boundaries, and the difficulty of simultaneously improving segmentation accuracy while maintaining a balanced trade-off between Recall and Precision. To address these issues, this article proposes a novel residual “Enhancement-Calibration” U-Net architecture, termed REC-UNet. The model consists of two task-specific modules: a Residual Enhancement Module (REM) and a Calibration Module (CM). REM leverages residual connections and cross-dimensional attention to enhance tumor feature representation for accurate segmentation, thereby establishing a foundation for balancing Recall and Precision. CM further mitigates noise propagation from shallow to deep feature layers, refining segmentation precision while sustaining high levels of both Recall and Precision. Experiments on the LiTS2017 and MSD_Task08 liver tumor datasets demonstrate that REC-UNet achieves superior performance over mainstream models, with a 4.34% improvement in Dice and a 4.24% improvement in Intersection over Union (IoU) over the second-best model (VM-UNet) on LiTS2017. We further validate the model on an in-house clinical liver tumor Magnetic Resonance Imaging (MRI) dataset, where it attains a Dice score of 87.88% and an IoU of 86.91%, while maintaining a well-balanced trade-off between Recall and Precision. Importantly, REC-UNet achieves high overall segmentation accuracy across diverse lesion sizes and contrast conditions without relying on explicit size-stratified optimization. These results confirm the robust generalizability of REC-UNet and highlight its significant clinical value for computer-assisted liver tumor diagnosis.

CERD3D-UNet: context-enhanced residual 3D U-Net with dual-attention gates and hyperparameter optimization for multimodal brain tumor segmentation