Hepatocellular carcinoma (HCC) is the third leading cause of cancer-related deaths worldwide, necessitating accurate and early diagnosis to guide therapy, along with assessment of treatment response. Response assessment criteria have evolved from traditional morphologic approaches, such as WHO criteria and Response Evaluation Criteria in Solid Tumors (RECIST), to more recent methods focused on evaluating viable tumor burden, including European Association for Study of Liver (EASL) criteria, modified RECIST (mRECIST) and Liver Imaging Reporting and Data System (LI-RADS) Treatment Response (LI-TR) algorithm. This shift reflects the complex and evolving landscape of HCC treatment in the context of emerging systemic and locoregional therapies. Each of these criteria have their own nuanced strengths and limitations in capturing the detailed characteristics of HCC treatment and response assessment. The emergence of functional imaging techniques, including dual-energy CT, perfusion imaging, and rising use of radiomics, are enhancing the capabilities of response assessment. Growth in the realm of artificial intelligence and machine learning models provides an opportunity to refine the precision of response assessment by facilitating analysis of complex imaging data patterns. This review article provides a comprehensive overview of existing criteria, discusses functional and emerging imaging techniques, and outlines future directions for advancing HCC tumor response assessment.

Transformers and state space sequence models (SSMs) have attracted interest in biomedical image segmentation for their ability to capture long-range dependency. However, traditional visual state space (VSS) methods suffer from the incompatibility of image tokens with autoregressive assumption. Although Transformer attention does not require this assumption, its high computational cost limits effective channel-wise information utilization. To overcome these limitations, we propose the Mamba-Like Aggregated UNet (MLAgg-UNet), which introduces Mamba-inspired mechanism to enrich Transformer channel representation and exploit implicit autoregressive characteristic within U-shaped architecture. For establishing dependencies among image tokens in single scale, the Mamba-Like Aggregated Attention (MLAgg) block is designed to balance representational ability and computational efficiency. Inspired by the human foveal vision system, Mamba macro-structure, and differential attention, MLAgg block can slide its focus over each image token, suppress irrelevant tokens, and simultaneously strengthen channel-wise information utilization. Moreover, leveraging causal relationships between consecutive low-level and high-level features in U-shaped architecture, we propose the Multi-Scale Mamba Module with Implicit Causality (MSMM) to optimize complementary information across scales. Embedded within skip connections, this module enhances semantic consistency between encoder and decoder features. Extensive experiments on four benchmark datasets, including AbdomenMRI, ACDC, BTCV, and EndoVis17, which cover MRI, CT, and endoscopy modalities, demonstrate that the proposed MLAgg-UNet consistently outperforms state-of-the-art CNN-based, Transformer-based, and Mamba-based methods. Specifically, it achieves improvements of at least 1.24%, 0.20%, 0.33%, and 0.39% in DSC scores on these datasets, respectively. These results highlight the model's ability to effectively capture feature correlations and integrate complementary multi-scale information, providing a robust solution for medical image segmentation. The implementation is publicly available at https://github.com/aticejiang/MLAgg-UNet.

Magnetic resonance (MR)-to-computed tomography (CT) translation offers significant advantages, including the elimination of radiation exposure associated with CT scans and the mitigation of imaging artifacts caused by patient motion. The existing approaches are based on single-modality MR-to-CT translation, with limited research exploring multimodal fusion. To address this limitation, we introduce Multi-modal MR to CT (MM2CT) translation method by leveraging multimodal T1- and T2-weighted MRI data, an innovative Mamba-based framework for multi-modal medical image synthesis. Mamba effectively overcomes the limited local receptive field in CNNs and the high computational complexity issues in Transformers. MM2CT leverages this advantage to maintain long-range dependencies modeling capabilities while achieving multi-modal MR feature integration. Additionally, we incorporate a dynamic local convolution module and a dynamic enhancement module to improve MRI-to-CT synthesis. The experiments on a public pelvis dataset demonstrate that MM2CT achieves state-of-the-art performance in terms of Structural Similarity Index Measure (SSIM) and Peak Signal-to-Noise Ratio (PSNR). Our code is publicly available at https://github.com/Gots-ch/MM2CT.

Medical image segmentation plays a crucial role in AI-assisted diagnostics, surgical planning, and treatment monitoring. Accurate and robust segmentation models are essential for enabling reliable, data-driven clinical decision making across diverse imaging modalities. Given the inherent variability in image characteristics across modalities, developing a unified model capable of generalizing effectively to multiple modalities would be highly beneficial. This model could streamline clinical workflows and reduce the need for modality-specific training. However, real-world deployment faces major challenges, including data scarcity, domain shift between modalities (e.g., CT vs. MRI), and privacy restrictions that prevent data sharing. To address these issues, we propose FedGIN, a Federated Learning (FL) framework that enables multimodal organ segmentation without sharing raw patient data. Our method integrates a lightweight Global Intensity Non-linear (GIN) augmentation module that harmonizes modality-specific intensity distributions during local training. We evaluated FedGIN using two types of datasets: an imputed dataset and a complete dataset. In the limited dataset scenario, the model was initially trained using only MRI data, and CT data was added to assess its performance improvements. In the complete dataset scenario, both MRI and CT data were fully utilized for training on all clients. In the limited-data scenario, FedGIN achieved a 12 to 18% improvement in 3D Dice scores on MRI test cases compared to FL without GIN and consistently outperformed local baselines. In the complete dataset scenario, FedGIN demonstrated near-centralized performance, with a 30% Dice score improvement over the MRI-only baseline and a 10% improvement over the CT-only baseline, highlighting its strong cross-modality generalization under privacy constraints.

This study aims to enhance the accuracy of fetal ultrasound image classification using convolutional neural networks, specifically EfficientNet. The research focuses on data collection, preprocessing, model training, and evaluation at different pregnancy stages: early, midterm, and newborn. EfficientNet showed the best performance, particularly in the newborn stage, demonstrating deep learning's potential to improve classification performance and support clinical workflows.

Renal tumors require early diagnosis and precise localization for effective treatment. This study aims to automate renal tumor analysis in abdominal CT images using a cascade 3D U-Net architecture for semantic kidney segmentation. To address challenges like edge detection and small object segmentation, the framework incorporates residual blocks to enhance convergence and efficiency. Comprehensive training configurations, preprocessing, and postprocessing strategies were employed to ensure accurate results. Tested on KiTS2019 data, the method ranked 23rd on the leaderboard (Nov 2024), demonstrating the enhanced cascade 3D U-Net's effectiveness in improving segmentation precision.

This study presents an AI framework for real-time NAFLD detection using ultrasound imaging, addressing operator dependency, imaging variability, and class imbalance. It integrates CNNs with machine learning classifiers and applies preprocessing techniques, including normalization and GAN-based augmentation, to enhance prediction for underrepresented disease stages. Grad-CAM provides visual explanations to support clinical interpretation. Trained on 10,352 annotated images from multiple Saudi centers, the framework achieved 98.9% accuracy and an AUC of 0.99, outperforming baseline CNNs by 12.4% and improving sensitivity for advanced fibrosis and subtle features. Future work will extend multi-class classification, validate performance across settings, and integrate with clinical systems.

Objective&#xD;This study assesses rectum contours generated using a commercial deep learning auto-contouring model and compares them to clinician contours using geometry, changes in dosimetry and toxicity modelling. &#xD;Approach&#xD;This retrospective study involved 308 prostate cancer patients who were treated using 3D-conformal radiotherapy. Computed tomography images were input into Limbus Contour (v1.8.0b3) to generate auto-contour structures for each patient. Auto-contours were not edited after their generation.&#xD;Rectum auto-contours were compared to clinician contours geometrically and dosimetrically. Dice similarity coefficient (DSC), mean Hausdorff distance (HD) and volume difference were assessed. Dose-volume histogram (DVH) constraints (V41%-V100%) were compared, and a Wilcoxon signed rank test was used to evaluate statistical significance of differences. &#xD;Toxicity modelling to compare contours was carried out using equivalent uniform dose (EUD) and clinical factors of abdominal surgery and atrial fibrillation. Trained models were tested (80:20) in their prediction of grade 1 late rectal bleeding (ntotal=124) using area-under the receiver operating characteristic curve (AUC).&#xD;Main results&#xD;Median DSC (interquartile range (IQR)) was 0.85 (0.09), median HD was 1.38 mm (0.60 mm) and median volume difference was -1.73 cc (14.58 cc). Median DVH differences between contours were found to be small (<1.5%) for all constraints although systematically larger than clinician contours (p<0.05). However, an IQR up to 8.0% was seen for individual patients across all dose constraints.&#xD;Models using EUD alone derived from clinician or auto-contours had AUCs of 0.60 (0.10) and 0.60 (0.09). AUC for models involving clinical factors and dosimetry was 0.65 (0.09) and 0.66 (0.09) when using clinician contours and auto-contours.&#xD;Significance&#xD;Although median DVH metrics were similar, variation for individual patients highlights the importance of clinician review. Rectal bleeding prediction accuracy did not depend on the contour method for this cohort. The auto-contouring model used in this study shows promise in a supervised workflow.&#xD.

To develop and evaluate a deep learning framework for automatic kidney and fluid segmentation in renal ultrasound images, aiming to enhance diagnostic accuracy and reduce variability in hydronephrosis assessment. A dataset of 1,731 renal ultrasound images, annotated by four experienced urologists, was used for model training and evaluation. The proposed framework integrates a DenseNet201 backbone, Feature Pyramid Network (FPN), and Self-Organizing Neural Network (SelfONN) layers to enable multi-scale feature extraction and improve spatial precision. Several architectures were tested under identical conditions to ensure fair comparison. Segmentation performance was assessed using standard metrics, including Dice coefficient, precision, and recall. The framework also supported hydronephrosis classification using the fluid-to-kidney area ratio, with a threshold of 0.213 derived from prior literature. The model achieved strong segmentation performance for kidneys (Dice: 0.92, precision: 0.93, recall: 0.91) and fluid regions (Dice: 0.89, precision: 0.90, recall: 0.88), outperforming baseline methods. The classification accuracy for detecting hydronephrosis reached 94%, based on the computed fluid-to-kidney ratio. Performance was consistent across varied image qualities, reflecting the robustness of the overall architecture. This study presents an automated, objective pipeline for analyzing renal ultrasound images. The proposed framework supports high segmentation accuracy and reliable classification, facilitating standardized and reproducible hydronephrosis assessment. Future work will focus on model optimization and incorporating explainable AI to enhance clinical integration.

To validate a deep learning (DL) reconstruction technique for faster post-contrast enhanced coronal Volume Interpolated Breath-hold Examination (VIBE) sequences and assess its image quality compared to conventionally acquired coronal VIBE sequences. This prospective study included 151 patients undergoing clinically indicated upper abdominal MRI acquired on 3 T scanners. Two coronal T1 fat-suppressed VIBE sequences were acquired: a DL-reconstructed sequence (VIBE_DL) and a standard sequence (VIBE_SD). Three radiologists independently evaluated six image quality parameters: overall image quality, perceived signal-to-noise ratio, severity of artifacts, liver edge sharpness, liver vessel sharpness, and lesion conspicuity, using a 4-point Likert scale. Inter-reader agreement was assessed using Gwet's AC2. Ordinal mixed-effects regression models were used to compare VIBE_DL and VIBE_SD. Acquisition times were 10.2 s for VIBE_DL compared to 22.3 s for VIBE_SD. VIBE_DL demonstrated superior overall image quality (OR 1.95, 95 % CI: 1.44-2.65, p < 0.001), reduced image noise (OR 3.02, 95 % CI: 2.26-4.05, p < 0.001), enhanced liver edge sharpness (OR 3.68, 95 % CI: 2.63-5.15, p < 0.001), improved liver vessel sharpness (OR 4.43, 95 % CI: 3.13-6.27, p < 0.001), and better lesion conspicuity (OR 9.03, 95 % CI: 6.34-12.85, p < 0.001) compared to VIBE_SD. However, VIBE_DL showed increased severity of peripheral artifacts (OR 0.13, p < 0.001). VIBE_DL detected 137/138 (99.3 %) focal liver lesions, while VIBE_SD detected 131/138 (94.9 %). Inter-reader agreement ranged from good to very good for both sequences. The DL-reconstructed VIBE sequence significantly outperformed the standard breath-hold VIBE in image quality and lesion detection, while reducing acquisition time. This technique shows promise for enhancing the diagnostic capabilities of contrast-enhanced abdominal MRI.