Deformable image registration (DIR) is an enabling technology in many diagnostic and therapeutic tasks. Despite this, DIR algorithms have limited clinical use, largely due to a lack of benchmark datasets for quality assurance during development. DIRs of intra-patient abdominal CTs are among the most challenging registration scenarios due to significant organ deformations and inconsistent image content. To support future algorithm development, here we introduce our first-of-its-kind abdominal CT DIR benchmark dataset, comprising large numbers of highly accurate landmark pairs on matching blood vessel bifurcations. Abdominal CT image pairs of 30 patients were acquired from several publicly available repositories as well as the authors' institution with IRB approval. The two CTs of each pair were originally acquired for the same patient but on different days. An image processing workflow was developed and applied to each CT image pair: (1) Abdominal organs were segmented with a deep learning model, and image intensity within organ masks was overwritten. (2) Matching image patches were manually identified between two CTs of each image pair. (3) Vessel bifurcation landmarks were labeled on one image of each image patch pair. (4) Image patches were deformably registered, and landmarks were projected onto the second image. (5) Landmark pair locations were refined manually or with an automated process. This workflow resulted in 1895 total landmark pairs, or 63 per case on average. Estimates of the landmark pair accuracy using digital phantoms were 0.7 mm ± 1.2 mm. The data are published in Zenodo at https://doi.org/10.5281/zenodo.14362785. Instructions for use can be found at https://github.com/deshanyang/Abdominal-DIR-QA. This dataset is a first-of-its-kind for abdominal DIR validation. The number, accuracy, and distribution of landmark pairs will allow for robust validation of DIR algorithms with precision beyond what is currently available.

Liver cirrhosis represents the end stage of chronic liver disease, characterized by extensive fibrosis and nodular regeneration that significantly increases mortality risk. While magnetic resonance imaging (MRI) offers a non-invasive assessment, accurately segmenting cirrhotic livers presents substantial challenges due to morphological alterations and heterogeneous signal characteristics. Deep learning approaches show promise for automating these tasks, but progress has been limited by the absence of large-scale, annotated datasets. Here, we present CirrMRI600+, the first comprehensive dataset comprising 628 high-resolution abdominal MRI scans (310 T1-weighted and 318 T2-weighted sequences, totaling nearly 40,000 annotated slices) with expert-validated segmentation labels for cirrhotic livers. The dataset includes demographic information, clinical parameters, and histopathological validation where available. Additionally, we provide benchmark results from 11 state-of-the-art deep learning experiments to establish performance standards. CirrMRI600+ enables the development and validation of advanced computational methods for cirrhotic liver analysis, potentially accelerating progress toward automated Cirrhosis visual staging and personalized treatment planning.

The integration of artificial intelligence (AI) with radiology signifies a transformative era in medicine. Vision foundation models have been adopted to enhance radiologic imaging analysis. However, the inherent complexities of 2D and 3D radiologic data present unique challenges that existing models, which are typically pretrained on general nonmedical images, do not adequately address. To bridge this gap and harness the diagnostic precision required in radiologic imaging, we introduce radiologic contrastive language-image pretraining (RadCLIP): a cross-modal vision-language foundational model that utilizes a vision-language pretraining (VLP) framework to improve radiologic image analysis. Building on the contrastive language-image pretraining (CLIP) approach, RadCLIP incorporates a slice pooling mechanism designed for volumetric image analysis and is pretrained using a large, diverse dataset of radiologic image-text pairs. This pretraining effectively aligns radiologic images with their corresponding text annotations, resulting in a robust vision backbone for radiologic imaging. Extensive experiments demonstrate RadCLIP's superior performance in both unimodal radiologic image classification and cross-modal image-text matching, underscoring its significant promise for enhancing diagnostic accuracy and efficiency in clinical settings. Our key contributions include curating a large dataset featuring diverse radiologic 2D/3D image-text pairs, pretraining RadCLIP as a vision-language foundation model on this dataset, developing a slice pooling adapter with an attention mechanism for integrating 2D images, and conducting comprehensive evaluations of RadCLIP on various radiologic downstream tasks.

Ultrasound imaging is pivotal in clinical diagnostics due to its affordability, portability, safety, real-time capability, and non-invasive nature. It is widely utilized for examining various organs, such as the breast, thyroid, ovary, cardiac, and more. However, the manual interpretation and annotation of ultrasound images are time-consuming and prone to variability among physicians. While single-task artificial intelligence (AI) solutions have been explored, they are not ideal for scaling AI applications in medical imaging. Foundation models, although a trending solution, often struggle with real-world medical datasets due to factors such as noise, variability, and the incapability of flexibly aligning prior knowledge with task adaptation. To address these limitations, we propose an orchestration learning framework named PerceptGuide for general-purpose ultrasound classification and segmentation. Our framework incorporates a novel orchestration mechanism based on prompted hyper-perception, which adapts to the diverse inductive biases required by different ultrasound datasets. Unlike self-supervised pre-trained models, which require extensive fine-tuning, our approach leverages supervised pre-training to directly capture task-relevant features, providing a stronger foundation for multi-task and multi-organ ultrasound imaging. To support this research, we compiled a large-scale Multi-task, Multi-organ public ultrasound dataset (M²-US), featuring images from 9 organs and 16 datasets, encompassing both classification and segmentation tasks. Our approach employs four specific prompts-Object, Task, Input, and Position-to guide the model, ensuring task-specific adaptability. Additionally, a downstream synchronization training stage is introduced to fine-tune the model for new data, significantly improving generalization capabilities and enabling real-world applications. Experimental results demonstrate the robustness and versatility of our framework in handling multi-task and multi-organ ultrasound image processing, outperforming both specialist models and existing general AI solutions. Compared to specialist models, our method improves segmentation from 82.26% to 86.45%, classification from 71.30% to 79.08%, while also significantly reducing model parameters.

Planning an osteotomy or arthroplasty surgery on a lower limb requires prior classification/identification of its deformities. The detection of skeletal landmarks and the calculation of angles required to identify the deformities are traditionally done manually, with measurement accuracy relying considerably on the experience of the individual doing the measurements. We propose a novel, image pyramid-based approach to skeletal landmark detection. The proposed approach uses a Convolutional Neural Network (CNN) that receives the raw X-ray image as input and produces the coordinates of the landmarks. The landmark estimations are modified iteratively via the error feedback method to come closer to the target. Our clinically produced full-leg X-Rays dataset is made publically available and used to train and test the network. Angular quantities are calculated based on detected landmarks. Angles are then classified as lower than normal, normal or higher than normal according to predefined ranges for a normal condition. The performance of our approach is evaluated at several levels: landmark coordinates accuracy, angles' measurement accuracy, and classification accuracy. The average absolute error (difference between automatically and manually determined coordinates) for landmarks was 0.79 ± 0.57 mm on test data, and the average absolute error (difference between automatically and manually calculated angles) for angles was 0.45 ± 0.42°. Results from multiple case studies involving high-resolution images show that the proposed approach outperforms previous deep learning-based approaches in terms of accuracy and computational cost. It also enables the automatic detection of the lower limb misalignments in full-leg x-ray images.

Accurate patient dosimetry estimates from fluoroscopically-guided interventions (FGIs) are hindered by limited knowledge of the specific anatomy that was irradiated. Current methods use data reported by the equipment to estimate the patient anatomy exposed during each irradiation event. We propose a deep learning algorithm to automatically match 2D fluoroscopic images with corresponding anatomical regions in computational phantoms, enabling more precise patient dose estimates. Our method involves two main steps: (1) simulating 2D fluoroscopic images, and (2) developing a deep learning algorithm to predict anatomical coordinates from these images. For part (1), we utilized DeepDRR for fast and realistic simulation of 2D x-ray images from 3D computed tomography datasets. We generated a diverse set of simulated fluoroscopic images from various regions with different field sizes. In part (2), we employed a Residual Neural Network (ResNet) architecture combined with metadata processing to effectively integrate patient-specific information (age and gender) to learn the transformation between 2D images and specific anatomical coordinates in each representative phantom. For the Modified ResNet model, we defined an allowable error range of ± 10 mm. The proposed method achieved over 90% of predictions within ± 10 mm, with strong alignment between predicted and true coordinates as confirmed by Bland-Altman analysis. Most errors were within ± 2%, with outliers beyond ± 5% primarily in Z-coordinates for infant phantoms due to their limited representation in the training data. These findings highlight the model's accuracy and its potential for precise spatial localization, while emphasizing the need for improved performance in specific anatomical regions. In this work, a comprehensive simulated 2D fluoroscopy image dataset was developed, addressing the scarcity of real clinical datasets and enabling effective training of deep-learning models. The modified ResNet successfully achieved precise prediction of anatomical coordinates from the simulated fluoroscopic images, enabling the goal of more accurate patient-specific dosimetry.

Developmental Dysplasia of the Hip (DDH) stands as one of the preeminent hip disorders prevalent in pediatric orthopedics. Automated diagnostic instruments, driven by artificial intelligence methodologies, are capable of providing substantial assistance to clinicians in the diagnosis of DDH. We have developed a dataset designated as Multitasking DDH (MTDDH), which is composed of two sub-datasets. Dataset 1 encompasses 1,250 pelvic X-ray images, with annotations demarcating four discrete regions for the evaluation of pelvic X-ray quality, in tandem with eight pivotal points serving as support for DDH diagnosis. Dataset 2 contains 906 pelvic X-ray images, and each image has been annotated with eight key points for assisting in the diagnosis of DDH. Notably, MTDDH represents the pioneering dataset engineered for the comprehensive evaluation of pelvic X-ray quality while concurrently offering the most exhaustive set of eight key points to bolster DDH diagnosis, thus fulfilling the exigency for enhanced diagnostic precision. Ultimately, we presented the elaborate process of constructing the MTDDH and furnished a concise introduction regarding its application.

Due to the association with higher incidence of left ventricular dysfunction and complications, segmentation of left ventricle and related pathological tissues: microvascular obstruction and myocardial infarction from late gadolinium enhancement cardiac magnetic resonance images is crucially important. However, lack of datasets, diverse shapes and locations, extreme imbalanced class, severe intensity distribution overlapping are the main challenges. We first release a late gadolinium enhancement cardiac magnetic resonance benchmark dataset LGE-LVP containing 140 patients with left ventricle myocardial infarction and concomitant microvascular obstruction. Then, a progressive deep learning model LVPSegNet is proposed to segment the left ventricle and its pathologies via adaptive region of interest extraction, sample augmentation, curriculum learning, and multiple receptive field fusion in dealing with the challenges. Comprehensive comparisons with state-of-the-art models on the internal and external datasets demonstrate that the proposed model performs the best on both geometric and clinical metrics and it most closely matched the clinician's performance. Overall, the released LGE-LVP dataset alongside the LVPSegNet we proposed offer a practical solution for automated left ventricular and its pathologies segmentation by providing data support and facilitating effective segmentation. The dataset and source codes will be released via https://github.com/DFLAG-NEU/LVPSegNet.

Background and objectiveCOVID-19 is considered as the biggest global health disaster in the 21st century, and it has a huge impact on the world.MethodsThis paper publishes a publicly available dataset of CT images of multiple types of pneumonia (COVID-19CT+). Specifically, the dataset contains 409,619 CT images of 1333 patients, with subset-A containing 312 community-acquired pneumonia cases and subset-B containing 1021 COVID-19 cases. In order to demonstrate that there are differences in the methods used to classify COVID-19CT+ images across time, we selected 13 classical machine learning classifiers and 5 deep learning classifiers to test the image classification task.ResultsIn this study, two sets of experiments are conducted using traditional machine learning and deep learning methods, the first set of experiments is the classification of COVID-19 in Subset-B versus COVID-19 white lung disease, and the second set of experiments is the classification of community-acquired pneumonia in Subset-A versus COVID-19 in Subset-B, demonstrating that the different periods of the methods differed on COVID-19CT+. On the first set of experiments, the accuracy of traditional machine learning reaches a maximum of 97.3% and a minimum of only 62.6%. Deep learning algorithms reaches a maximum of 97.9% and a minimum of 85.7%. On the second set of experiments, traditional machine learning reaches a high of 94.6% accuracy and a low of 56.8%. The deep learning algorithm reaches a high of 91.9% and a low of 86.3%.ConclusionsThe COVID-19CT+ in this study covers a large number of CT images of patients with COVID-19 and community-acquired pneumonia and is one of the largest datasets available. We expect that this dataset will attract more researchers to participate in exploring new automated diagnostic algorithms to contribute to the improvement of the diagnostic accuracy and efficiency of COVID-19.

Lung cancer has been one of the major threats across the world with the highest mortalities. Computer-aided detection (CAD) can help in early detection and thus can help increase the survival rate. Accurate lung parenchyma segmentation (to include the juxta-pleural nodules) and lung nodule segmentation, the primary symptom of lung cancer, play a crucial role in the overall accuracy of the Lung CAD pipeline. Lung nodule segmentation is quite challenging because of the diverse nodule types and other inhibit structures present within the lung lobes. Traditional machine/deep learning methods suffer from generalization and robustness. Recent Vision Language Models/Foundation Models perform well on the anatomical level, but they suffer on fine-grained segmentation tasks, and their semi-automatic nature limits their effectiveness in real-time clinical scenarios. In this paper, we propose a novel method for accurate 3D segmentation of lung parenchyma and lung nodules. The proposed architecture is an attention-based network with residual blocks at each encoder-decoder state. Max pooling is replaced by strided convolutions at the encoder, and trilinear interpolation is replaced by transposed convolutions at the decoder to maximize the number of learnable parameters. Dilated convolutions at each encoder-decoder stage allow the model to capture the larger context without increasing computational costs. The proposed method has been evaluated extensively on one of the largest publicly available datasets, namely LUNA16, and is compared with recent notable work in the domain using standard performance metrics like Dice score, IOU, etc. It can be seen from the results that the proposed method achieves better performance than state-of-the-art methods. The source code, datasets, and pre-processed data can be accessed using the link: https://github.com/EMeRALDsNRPU/Attention-Based-3D-ResUNet.