Magnetic Resonance Imaging (MRI) is indispensable for modern diagnostics because of its detailed anatomical and functional information without the use of ionizing radiation. However, acquiring multiple imaging sequences - such as T1-weighted (T1w) and T2-weighted (T2w) scans - can prolong scan times, increase patient discomfort, and raise healthcare costs. In this study, we propose an unsupervised framework based on a contrast-sensitive domain translation network with adaptive feature normalization to translate unpaired T2w MRI images into clinically acceptable T1w images. Our method employs adversarial training, along with cycle consistency, identity, and attention-guided loss functions. These components ensure that the generated images not only preserve essential anatomical details but also exhibit high visual fidelity compared to ground truth T1w images. Quantitative evaluation on a publicly available MRI dataset yielded a mean Peak Signal-to-Noise Ratio (PSNR) of 22.403 dB, a mean Structural Similarity Index (SSIM) of 0.775, Root Mean Squared Error (RMSE) of 0.078, and Mean Absolute Error (MAE) of 0.036. Additional analysis of pixel intensity and grayscale distributions further supported the consistency between the generated and ground truth images. Qualitative assessment included visual comparison to assess perceptual fidelity. These promising results suggest that a contrast-sensitive domain translation network with an adaptive feature normalization framework can effectively generate realistic T1w images from T2w inputs, potentially reducing the need for acquiring multiple sequences and thereby streamlining MRI protocols.

Radiologists currently have very limited and time-consuming options to annotate findings on the images and are mostly limited to arrows, calipers and lines to annotate any type of findings on most PACS systems. We propose a framework placing encoded, transferable, highly contextual structured text annotations directly on PACS images indicating the type of lesion, level of suspicion, location, lesion measurement, and TNM status for malignant lesions, along with automated integration of this information into the radiology report. This approach offers a one-stop solution to generate radiology reports that are easily understood by other radiologists, patient care providers, patients, and machines while reducing the effort needed to dictate a detailed radiology report and minimizing speech recognition errors. It also provides a framework for automated generation of large volume high quality annotated data sets for machine learning algorithms from daily work of radiologists. Enabling voice dictation of these contextual annotations directly into PACS similar to voice enabled Google search will further enhance the user experience. Wider adaptation of contextualized structured annotations in the future can facilitate studies understanding the temporal evolution of different tumor lesions across multiple lines of treatment and early detection of asynchronous response/areas of treatment failure. We present a futuristic vision, and solution with the potential to transform clinical work and research in oncologic imaging.

Accurate medical image segmentation remains challenging due to blurred lesion boundaries (LBA), loss of high-frequency details (LHD), and difficulty in modeling long-range anatomical structures (DC-LRSS). Vision Mamba employs one-dimensional causal state-space recurrence to efficiently model global dependencies, thereby substantially mitigating DC-LRSS. However, its patch tokenization and 1D serialization disrupt local pixel adjacency and impose a low-pass filtering effect, resulting in Local High-frequency Information Capture Deficiency (LHICD) and two-dimensional Spatial Structure Degradation (2D-SSD), which in turn exacerbate LBA and LHD. In this work, we propose FaRMamba, a novel extension that explicitly addresses LHICD and 2D-SSD through two complementary modules. A Multi-Scale Frequency Transform Module (MSFM) restores attenuated high-frequency cues by isolating and reconstructing multi-band spectra via wavelet, cosine, and Fourier transforms. A Self-Supervised Reconstruction Auxiliary Encoder (SSRAE) enforces pixel-level reconstruction on the shared Mamba encoder to recover full 2D spatial correlations, enhancing both fine textures and global context. Extensive evaluations on CAMUS echocardiography, MRI-based Mouse-cochlea, and Kvasir-Seg endoscopy demonstrate that FaRMamba consistently outperforms competitive CNN-Transformer hybrids and existing Mamba variants, delivering superior boundary accuracy, detail preservation, and global coherence without prohibitive computational overhead. This work provides a flexible frequency-aware framework for future segmentation models that directly mitigates core challenges in medical imaging.

Medical image segmentation plays an important role in computer-aided diagnosis. Traditional convolution-based U-shape segmentation architectures are usually limited by the local receptive field. Existing vision transformers have been widely applied to diverse medical segmentation frameworks due to their superior capabilities of capturing global contexts. Despite the advantage, the real-world application of vision transformers is challenged by their non-linear self-attention mechanism, requiring huge computational costs. To address this issue, the selective state space model (SSM) Mamba has gained recognition for its adeptness in modeling long-range dependencies in sequential data, particularly noted for its efficient memory costs. In this paper, we propose MambaVesselNet++, a Hybrid CNN-Mamba framework for medical image segmentation. Our MambaVesselNet++ is comprised of a hybrid image encoder (Hi-Encoder) and a bifocal fusion decoder (BF-Decoder). In Hi-Encoder, we first devise the texture-aware layer to capture low-level semantic features by leveraging convolutions. Then, we utilize Mamba to effectively model long-range dependencies with linear complexity. The Bi-Decoder adopts skip connections to combine local and global information of the Hi-Encoder for the accurate generation of segmentation masks. Extensive experiments demonstrate that MambaVesselNet++ outperforms current convolution-based, transformer-based, and Mamba-based state-of-the-arts across diverse medical 2D, 3D, and instance segmentation tasks. The code is available at https://github.com/CC0117/MambaVesselNet.

All-in-one medical image restoration (MedIR) aims to address multiple MedIR tasks using a unified model, concurrently recovering various high-quality (HQ) medical images (e.g., MRI, CT, and PET) from low-quality (LQ) counterparts. However, all-in-one MedIR presents significant challenges due to the heterogeneity across different tasks. Each task involves distinct degradations, leading to diverse information losses in LQ images. Existing methods struggle to handle these diverse information losses associated with different tasks. To address these challenges, we propose a latent diffusion-enhanced vector-quantized codebook prior and develop \textbf{DiffCode}, a novel framework leveraging this prior for all-in-one MedIR. Specifically, to compensate for diverse information losses associated with different tasks, DiffCode constructs a task-adaptive codebook bank to integrate task-specific HQ prior features across tasks, capturing a comprehensive prior. Furthermore, to enhance prior retrieval from the codebook bank, DiffCode introduces a latent diffusion strategy that utilizes the diffusion model's powerful mapping capabilities to iteratively refine the latent feature distribution, estimating more accurate HQ prior features during restoration. With the help of the task-adaptive codebook bank and latent diffusion strategy, DiffCode achieves superior performance in both quantitative metrics and visual quality across three MedIR tasks: MRI super-resolution, CT denoising, and PET synthesis.

Recent advances in deep learning have significantly enhanced the performance of medical image segmentation. However, maintaining a balanced integration of feature localization, global context modeling, and computational efficiency remains a critical research challenge. Convolutional Neural Networks (CNNs) effectively capture fine-grained local features through hierarchical convolutions; however, they often struggle to model long-range dependencies due to their limited receptive field. Transformers address this limitation by leveraging self-attention mechanisms to capture global context, but they are computationally intensive and require large-scale data for effective training. The Mamba architecture has emerged as a promising approach, effectively capturing long-range dependencies while maintaining low computational overhead and high segmentation accuracy. Based on this, we propose a method named CLT-MambaSeg that integrates Convolution, Linear Transformer, and Multiscale Mamba architectures to capture local features, model global context, and improve computational efficiency for medical image segmentation. It utilizes a convolution-based Spatial Representation Extraction (SREx) module to capture intricate spatial relationships and dependencies. Further, it comprises a Mamba Vision Linear Transformer (MVLTrans) module to capture multiscale context, spatial and sequential dependencies, and enhanced global context. In addition, to address the problem of limited data, we propose a novel Memory-Guided Augmentation Generative Adversarial Network (MeGA-GAN) that generates synthetic realistic images to further enhance the segmentation performance. We conduct extensive experiments and ablation studies on the five benchmark datasets, namely CVC-ClinicDB, Breast UltraSound Images (BUSI), PH2, and two datasets from the International Skin Imaging Collaboration (ISIC), namely ISIC-2016 and ISIC-2017. Experimental results demonstrate the efficacy of the proposed CLT-MambaSeg compared to other state-of-the-art methods.

Anomaly detection in medical imaging plays a crucial role in identifying pathological regions across various imaging modalities, such as brain MRI, liver CT, and carotid ultrasound (US). However, training fully supervised segmentation models is often hindered by the scarcity of expert annotations and the complexity of diverse anatomical structures. To address these issues, we propose a novel unsupervised anomaly detection framework based on a diffusion model that incorporates a synthetic anomaly (Synomaly) noise function and a multi-stage diffusion process. Synomaly noise introduces synthetic anomalies into healthy images during training, allowing the model to effectively learn anomaly removal. The multi-stage diffusion process is introduced to progressively denoise images, preserving fine details while improving the quality of anomaly-free reconstructions. The generated high-fidelity counterfactual healthy images can further enhance the interpretability of the segmentation models, as well as provide a reliable baseline for evaluating the extent of anomalies and supporting clinical decision-making. Notably, the unsupervised anomaly detection model is trained purely on healthy images, eliminating the need for anomalous training samples and pixel-level annotations. We validate the proposed approach on brain MRI, liver CT datasets, and carotid US. The experimental results demonstrate that the proposed framework outperforms existing state-of-the-art unsupervised anomaly detection methods, achieving performance comparable to fully supervised segmentation models in the US dataset. Ablation studies further highlight the contributions of Synomaly noise and the multi-stage diffusion process in improving anomaly segmentation. These findings underscore the potential of our approach as a robust and annotation-efficient alternative for medical anomaly detection. Code:https://github.com/yuan-12138/Synomaly.

Precise 3D segmentation of paranasal sinuses is essential for accurate diagnosis and treatment. This study aimed to develop a fully automated segmentation algorithm for the paranasal sinuses using the nnU-Net v2 architecture. The nnU-Net v2-based segmentation algorithm was developed using Python 3.6.1 and the PyTorch library, and its performance was evaluated on a dataset of 97 cone-beam computed tomography (CBCT) scans. Ground truth annotations were manually generated by expert radiologists using the 3D Slicer software, employing a polygonal labeling technique across sagittal, coronal, and axial planes. Model performance was assessed using several quantitative metrics, including accuracy, Dice Coefficient (DC), sensitivity, precision, Jaccard Index, Area Under the Curve (AUC), and 95% Hausdorff Distance (95% HD). The nnU-Net v2-based algorithm demonstrated high segmentation performance across all paranasal sinuses. Dice Coefficient (DC) values were 0.94 for the frontal, 0.95 for the sphenoid, 0.97 for the maxillary, and 0.88 for the ethmoid sinuses. Accuracy scores exceeded 99% for all sinuses. The 95% Hausdorff Distance (95% HD) values were 0.51 mm for both the frontal and maxillary sinuses, 0.85 mm for the sphenoid sinus, and 1.17 mm for the ethmoid sinus. Jaccard indices were 0.90, 0.91, 0.94, and 0.80, respectively. This study highlights the high accuracy and precision of the nnU-Net v2-based CNN model in the fully automated segmentation of paranasal sinuses from CBCT images. The results suggest that the proposed model can significantly contribute to clinical decision-making processes, facilitating diagnostic and therapeutic procedures.

Purpose: Accurate tumor segmentation is vital for adaptive radiation therapy (ART) but remains time-consuming and user-dependent. Segment Anything Model 2 (SAM2) shows promise for prompt-based segmentation but struggles with tumor accuracy. We propose prior knowledge-based augmentation strategies to enhance SAM2 for ART. Methods: Two strategies were introduced to improve SAM2: (1) using prior MR images and annotations as contextual inputs, and (2) improving prompt robustness via random bounding box expansion and mask erosion/dilation. The resulting model, SAM2-Aug, was fine-tuned and tested on the One-Seq-Liver dataset (115 MRIs from 31 liver cancer patients), and evaluated without retraining on Mix-Seq-Abdomen (88 MRIs, 28 patients) and Mix-Seq-Brain (86 MRIs, 37 patients). Results: SAM2-Aug outperformed convolutional, transformer-based, and prompt-driven models across all datasets, achieving Dice scores of 0.86(liver), 0.89(abdomen), and 0.90(brain). It demonstrated strong generalization across tumor types and imaging sequences, with improved performance in boundary-sensitive metrics. Conclusions: Incorporating prior images and enhancing prompt diversity significantly boosts segmentation accuracy and generalizability. SAM2-Aug offers a robust, efficient solution for tumor segmentation in ART. Code and models will be released at https://github.com/apple1986/SAM2-Aug.

Radiography imaging protocols target on specific anatomical regions, resulting in highly consistent images with recurrent structural patterns across patients. Recent advances in medical anomaly detection have demonstrated the effectiveness of CNN- and transformer-based approaches. However, CNNs exhibit limitations in capturing long-range dependencies, while transformers suffer from quadratic computational complexity. In contrast, Mamba-based models, leveraging superior long-range modeling, structural feature extraction, and linear computational efficiency, have emerged as a promising alternative. To capitalize on the inherent structural regularity of medical images, this study introduces SP-Mamba, a spatial-perception Mamba framework for unsupervised medical anomaly detection. The window-sliding prototype learning and Circular-Hilbert scanning-based Mamba are introduced to better exploit consistent anatomical patterns and leverage spatial information for medical anomaly detection. Furthermore, we excavate the concentration and contrast characteristics of anomaly maps for improving anomaly detection. Extensive experiments on three diverse medical anomaly detection benchmarks confirm the proposed method's state-of-the-art performance, validating its efficacy and robustness. The code is available at https://github.com/Ray-RuiPan/SP-Mamba.