A brain tumor is an abnormal growth in the brain that disrupts its functionality and poses a significant threat to human life by damaging neurons. Early detection and classification of brain tumors are crucial to prevent complications and maintain good health. Recent advancements in deep learning techniques have shown immense potential in image classification and segmentation for tumor identification and classification. In this study, we present a platform, BrainView, for detection, and segmentation of brain tumors from Magnetic Resonance Images (MRI) using deep learning. We utilized EfficientNetB7 pre-trained model to design our proposed DeepBrainNet classification model for analyzing brain MRI images to classify its type. We also proposed a EfficinetNetB7 based image segmentation model, called the EffB7-UNet, for tumor localization. Experimental results show significantly high classification (99.96%) and segmentation (92.734%) accuracies for our proposed models. Finally, we discuss the contours of a cloud application for BrainView using Flask and Flutter to help researchers and clinicians use our machine learning models online for research purposes.

Breast cancer remains a leading cause of mortality among women worldwide, underscoring the need for accurate and timely diagnostic methods. Precise segmentation of nuclei in breast histopathology images is crucial for effective diagnosis and prognosis, offering critical insights into tumor characteristics and informing treatment strategies. This paper presents an enhanced U-Net architecture utilizing ResNet-34 as an advanced backbone, aimed at improving nuclei segmentation performance. The proposed model is evaluated and compared with standard U-Net and its other variants, including U-Net with VGG-16 and Inception-v3 backbones, using the BreCaHad dataset with nuclei masks generated through ImageJ software. The U-Net model with ResNet-34 backbone achieved superior performance, recording an Intersection over Union (IoU) score of 0.795, significantly outperforming the basic U-Net's IoU score of 0.725. The integration of advanced backbones and data augmentation techniques substantially improved segmentation accuracy, especially on limited medical imaging datasets. Comparative analysis demonstrated that ResNet-34 consistently surpassed other configurations across multiple metrics, including IoU, accuracy, precision, and F1 score. Further validation on the BNS and MoNuSeg-2018 datasets confirmed the robustness of the proposed model. This study highlights the potential of advanced deep learning architectures combined with augmentation methods to address challenges in nuclei segmentation, contributing to the development of more effective clinical diagnostic tools and improved patient care outcomes.

Medical image segmentation plays an important role in various clinical applications, but existing models often struggle with the computational inefficiencies and challenges posed by complex medical data. State Space Sequence Models (SSMs) have demonstrated promise in modeling long-range dependencies with linear computational complexity, yet their application in medical image segmentation remains hindered by incompatibilities with image tokens and autoregressive assumptions. Moreover, it is difficult to achieve a balance in capturing both local fine-grained information and global semantic dependencies. To address these challenges, we introduce SAMA-UNet, a novel architecture for medical image segmentation. A key innovation is the Self-Adaptive Mamba-like Aggregated Attention (SAMA) block, which integrates contextual self-attention with dynamic weight modulation to prioritise the most relevant features based on local and global contexts. This approach reduces computational complexity and improves the representation of complex image features across multiple scales. We also suggest the Causal-Resonance Multi-Scale Module (CR-MSM), which enhances the flow of information between the encoder and decoder by using causal resonance learning. This mechanism allows the model to automatically adjust feature resolution and causal dependencies across scales, leading to better semantic alignment between the low-level and high-level features in U-shaped architectures. Experiments on MRI, CT, and endoscopy images show that SAMA-UNet performs better in segmentation accuracy than current methods using CNN, Transformer, and Mamba. The implementation is publicly available at GitHub.

Liver cancer, especially hepatocellular carcinoma (HCC), remains one of the most fatal cancers globally, emphasizing the critical need for accurate tumor segmentation to enable timely diagnosis and effective treatment planning. Traditional imaging techniques, such as CT and MRI, rely on manual interpretation, which can be both time-intensive and subject to variability. This study introduces FasNet, an innovative hybrid deep learning model that combines ResNet-50 and VGG-16 architectures, incorporating Channel and Spatial Attention mechanisms alongside Monte Carlo Dropout to improve segmentation precision and reliability. FasNet leverages ResNet-50's robust feature extraction and VGG-16's detailed spatial feature capture to deliver superior liver tumor segmentation accuracy. Channel and spatial attention mechanisms could selectively focus on the most relevant features and spatial regions for suitable segmentation with good accuracy and reliability. Monte Carlo Dropout estimates uncertainty and adds robustness, which is critical for high-stakes medical applications. Tested on the LiTS17 dataset, FasNet achieved a Dice Coefficient of 0.8766 and a Jaccard Index of 0.8487, surpassing several state-of-the-art methods. The Channel and Spatial Attention mechanisms in FasNet enhance feature selection, focusing on the most relevant spatial and channel information, while Monte Carlo Dropout improves model robustness and uncertainty estimation. These results position FasNet as a powerful diagnostic tool, offering precise and automated liver tumor segmentation that aids in early detection and precise treatment, ultimately enhancing patient outcomes.

<i>"Just Accepted" papers have undergone full peer review and have been accepted for publication in <i>Radiology: Artificial Intelligence</i>. This article will undergo copyediting, layout, and proof review before it is published in its final version. Please note that during production of the final copyedited article, errors may be discovered which could affect the content.</i> Purpose To develop a deep learning segmentation model that can segment abdominal organs on CT and MR images with high accuracy and generalization ability. Materials and Methods In this study, an extended nnU-Net model was trained for abdominal organ segmentation. A domain randomization method in both the image and feature space was developed to improve the generalization ability under cross-site and cross-modality settings on public prostate MRI and abdominal CT and MRI datasets. The prostate MRI dataset contains data from multiple health care institutions with domain shifts. The abdominal CT and MRI dataset is structured for cross-modality evaluation, training on one modality (eg, MRI) and testing on the other (eg, CT). This domain randomization method was then used to train a segmentation model with enhanced generalization ability on the abdominal multiorgan segmentation challenge (AMOS) dataset to improve abdominal CT and MR multiorgan segmentation, and the model was compared with two commonly used segmentation algorithms (TotalSegmentator and MRSegmentator). Model performance was evaluated using the Dice similarity coefficient (DSC). Results The proposed domain randomization method showed improved generalization ability on the cross-site and cross-modality datasets compared with the state-of-the-art methods. The segmentation model using this method outperformed two other publicly available segmentation models on data from unseen test domains (Average DSC: 0.88 versus 0.79; <i>P</i> < .001 and 0.88 versus 0.76; <i>P</i> < .001). Conclusion The combination of image and feature domain randomizations improved the accuracy and generalization ability of deep learning-based abdominal segmentation on CT and MR images. © RSNA, 2025.

Medical image segmentation is critical for disease diagnosis, treatment planning, and prognosis assessment, yet the complexity and diversity of medical images pose significant challenges to accurate segmentation. While Convolutional Neural Networks capture local features and Vision Transformers excel in the global context, both struggle with efficient long-range dependency modeling. Inspired by Mamba's State Space Modeling efficiency, we propose ÆMMamba, a novel multi-scale feature extraction framework built on the Mamba backbone network. AÆMMamba integrates several innovative modules: the Efficient Fusion Bridge (EFB) module, which employs a bidirectional state-space model and attention mechanisms to fuse multi-scale features; the Edge-Aware Module (EAM), which enhances low-level edge representation using Sobel-based edge extraction; and the Boundary Sensitive Decoder (BSD), which leverages inverse attention and residual convolutional layers to handle cross-level complex boundaries. ÆMMamba achieves state-of-the-art performance across 8 medical segmentation datasets. On polyp segmentation datasets (Kvasir, ClinicDB, ColonDB, EndoScene, ETIS), it records the highest mDice and mIoU scores, outperforming methods like MADGNet and Swin-UMamba, with a standout mDice of 72.22 on ETIS, the most challenging dataset in this domain. For lung and breast segmentation, ÆMMamba surpasses competitors such as H2Former and SwinUnet, achieving Dice scores of 84.24 on BUSI and 79.83 on COVID-19 Lung. And on the LGG brain MRI dataset, ÆMMamba attains an mDice of 87.25 and an mIoU of 79.31, outperforming all compared methods. The source code will be released at https://github.com/xingbod/eMMamba.

Fast Healthcare Interoperability Resources (FHIR) is a widely used standard for storing and exchanging health care data. At the same time, image-based artificial intelligence (AI) models for quantifying relevant body structures and organs from routine computed tomography (CT)/magnetic resonance imaging scans have emerged. The missing link, simultaneously a needed step in advancing personalized medicine, is the incorporation of measurements delivered by AI models into an interoperable and standardized format. Incorporating image-based measurements and biomarkers into FHIR profiles can standardize data exchange, enabling timely, personalized treatment decisions and improving the precision and efficiency of patient care. This study aims to present the synergistic incorporation of CT-derived body organ and composition measurements with FHIR, delineating an initial paradigm for storing image-based biomarkers. This study integrated the results of the Body and Organ Analysis (BOA) model into FHIR profiles to enhance the interoperability of image-based biomarkers in radiology. The BOA model was selected as an exemplary AI model due to its ability to provide detailed body composition and organ measurements from CT scans. The FHIR profiles were developed based on 2 primary observation types: Body Composition Analysis (BCA Observation) for quantitative body composition metrics and Body Structure Observation for organ measurements. These profiles were structured to interoperate with a specially designed Diagnostic Report profile, which references the associated Imaging Study, ensuring a standardized linkage between image data and derived biomarkers. To ensure interoperability, all labels were mapped to SNOMED CT (Systematized Nomenclature of Medicine - Clinical Terms) or RadLex terminologies using specific value sets. The profiles were developed using FHIR Shorthand (FSH) and SUSHI, enabling efficient definition and implementation guide generation, ensuring consistency and maintainability. In this study, 4 BOA profiles, namely, Body Composition Analysis Observation, Body Structure Volume Observation, Diagnostic Report, and Imaging Study, have been presented. These FHIR profiles, which cover 104 anatomical landmarks, 8 body regions, and 8 tissues, enable the interoperable usage of the results of AI segmentation models, providing a direct link between image studies, series, and measurements. The BOA profiles provide a foundational framework for integrating AI-derived imaging biomarkers into FHIR, bridging the gap between advanced imaging analytics and standardized health care data exchange. By enabling structured, interoperable representation of body composition and organ measurements, these profiles facilitate seamless integration into clinical and research workflows, supporting improved data accessibility and interoperability. Their adaptability allows for extension to other imaging modalities and AI models, fostering a more standardized and scalable approach to using imaging biomarkers in precision medicine. This work represents a step toward enhancing the integration of AI-driven insights into digital health ecosystems, ultimately contributing to more data-driven, personalized, and efficient patient care.

Most machine learning-based image segmentation models produce pixel-wise confidence scores - typically derived from softmax outputs - that represent the model's predicted probability for each class label at every pixel. While this information can be particularly valuable in high-stakes domains such as medical imaging, these (uncalibrated) scores are heuristic in nature and do not constitute rigorous quantitative uncertainty estimates. Conformal prediction (CP) provides a principled framework for transforming heuristic confidence scores into statistically valid uncertainty estimates. However, applying CP directly to image segmentation ignores the spatial correlations between pixels, a fundamental characteristic of image data. This can result in overly conservative and less interpretable uncertainty estimates. To address this, we propose CONSIGN (Conformal Segmentation Informed by Spatial Groupings via Decomposition), a CP-based method that incorporates spatial correlations to improve uncertainty quantification in image segmentation. Our method generates meaningful prediction sets that come with user-specified, high-probability error guarantees. It is compatible with any pre-trained segmentation model capable of generating multiple sample outputs - such as those using dropout, Bayesian modeling, or ensembles. We evaluate CONSIGN against a standard pixel-wise CP approach across three medical imaging datasets and two COCO dataset subsets, using three different pre-trained segmentation models. Results demonstrate that accounting for spatial structure significantly improves performance across multiple metrics and enhances the quality of uncertainty estimates.

Automatic pancreas segmentation in CT scans is crucial for various medical applications, including early diagnosis and computer-assisted surgery. However, existing segmentation methods remain suboptimal due to significant pancreas size variations across slices and severe class imbalance caused by the pancreas's small size and CT scanner movement during imaging. Traditional computer vision techniques struggle with these challenges, while deep learning-based approaches, despite their success in other domains, still face limitations in pancreas segmentation. To address these issues, we propose a novel, three-stage workflow that enhances segmentation accuracy and computational efficiency. First, we introduce External Contour Cropping (ECC), a background cleansing technique that mitigates class imbalance. Second, we propose a Size Ratio (SR) technique that restructures the training dataset based on the relative size of the target organ, improving the robustness of the model against anatomical variations. Third, we develop MOMUNet, an ultra-lightweight segmentation model with only 1.31 million parameters, designed for optimal performance on limited computational resources. Our proposed workflow achieves an improvement in Dice Score (DSC) of 2.56% over state-of-the-art (SOTA) models in the NIH-Pancreas dataset and 2.97% in the MSD-Pancreas dataset. Furthermore, applying the proposed model to another small organ, such as colon cancer segmentation in the MSD-Colon dataset, yielded a DSC of 68.4%, surpassing the SOTA models. These results demonstrate the effectiveness of our approach in significantly improving segmentation accuracy for small abdomen organs including pancreas and colon, making deep learning more accessible for low-resource medical facilities.

This study centers around the competition between Convolutional Neural Networks (CNNs) with large convolutional kernels and Vision Transformers in the domain of computer vision, delving deeply into the issues pertaining to parameters and computational complexity that stem from the utilization of large convolutional kernels. Even though the size of the convolutional kernels has been extended up to 51×51, the enhancement of performance has hit a plateau, and moreover, striped convolution incurs a performance degradation. Enlightened by the hierarchical visual processing mechanism inherent in humans, this research innovatively incorporates a shared parameter mechanism for large convolutional kernels. It synergizes the expansion of the receptive field enabled by large convolutional kernels with the extraction of fine-grained features facilitated by small convolutional kernels. To address the surging number of parameters, a meticulously designed parameter sharing mechanism is employed, featuring fine-grained processing in the central region of the convolutional kernel and wide-ranging parameter sharing in the periphery. This not only curtails the parameter count and mitigates the model complexity but also sustains the model's capacity to capture extensive spatial relationships. Additionally, in light of the problems of spatial feature information loss and augmented memory access during the 1×1 convolutional channel compression phase, this study further puts forward a dynamic channel sampling approach, which markedly elevates the accuracy of tumor subregion segmentation. To authenticate the efficacy of the proposed methodology, a comprehensive evaluation has been conducted on three brain tumor segmentation datasets, namely BraTs2020, BraTs2024, and Medical Segmentation Decathlon Brain 2018. The experimental results evince that the proposed model surpasses the current mainstream ConvNet and Transformer architectures across all performance metrics, proffering novel research perspectives and technical stratagems for the realm of medical image segmentation.