Assessing collateral circulation is essential in determining the best treatment for ischemic stroke patients as good collaterals lead to different treatment options, i.e., thrombectomy, whereas poor collaterals can adversely affect the treatment by leading to excess bleeding and eventually death. To reduce inter- and intra-rater variability and save time in radiologist assessments, computer-aided methods, mainly using deep neural networks, have gained popularity. The current literature demonstrates effectiveness when using balanced and extensive datasets in deep learning; however, such data sets are scarce for stroke, and the number of data samples for poor collateral cases is often limited compared to those for good collaterals. We propose a novel approach called CASCADE-FSL to distinguish poor collaterals effectively. Using a small, unbalanced data set, we employ a few-shot learning approach for training using a 2D ResNet-50 as a backbone and designating good and intermediate cases as two normal classes. We identify poor collaterals as anomalies in comparison to the normal classes. Our novel approach achieves an overall accuracy, sensitivity, and specificity of 0.88, 0.88, and 0.89, respectively, demonstrating its effectiveness in addressing the imbalanced dataset challenge and accurately identifying poor collateral circulation cases.

Radiographic imaging is a non-invasive technique of considerable importance for evaluating tumor treatment response. However, redundancy in CT data and the lack of labeled data make it challenging to accurately assess the response of locally advanced rectal cancer (LARC) patients to neoadjuvant chemoradiotherapy (nCRT) using current imaging indicators. In this study, we propose a novel learning framework to automatically predict the response of LARC patients to nCRT. Specifically, we develop a deep learning network called the Expand Intensive Attention Network (EIA-Net), which enhances the network's feature extraction capability through cascaded 3D convolutions and coordinate attention. Instance-oriented collaborative self-supervised learning (IOC-SSL) is proposed to leverage unlabeled data for training, reducing the reliance on labeled data. In a dataset consisting of 1,575 volumes, the proposed method achieves an AUC score of 0.8562. The dataset includes two distinct parts: the self-supervised dataset containing 1,394 volumes and the supervised dataset comprising 195 volumes. Analysis of the lifetime predictions reveals that patients with pathological complete response (pCR) predicted by EIA-Net exhibit better overall survival (OS) compared to non-pCR patients with LARC. The retrospective study demonstrates that imaging-based pCR prediction for patients with low rectal cancer can assist clinicians in making informed decisions regarding the need for Miles operation, thereby improving the likelihood of anal preservation, with an AUC of 0.8222. These results underscore the potential of our method to enhance clinical decision-making, offering a promising tool for personalized treatment and improved patient outcomes in LARC management.

Osteoarthritis is a degenerative condition that affects bones and cartilage, often leading to structural changes, including osteophyte formation, bone density loss, and the narrowing of joint spaces. Over time, this process may disrupt the glenohumeral (GH) joint functionality, requiring a targeted treatment. Various options are available to restore joint functions, ranging from conservative management to surgical interventions, depending on the severity of the condition. This work introduces an innovative deep learning framework to process shoulder CT scans. It features the semantic segmentation of the proximal humerus and scapula, the 3D reconstruction of bone surfaces, the identification of the GH joint region, and the staging of three common osteoarthritic-related conditions: osteophyte formation (OS), GH space reduction (JS), and humeroscapular alignment (HSA). Each condition was stratified into multiple severity stages, offering a comprehensive analysis of shoulder bone structure pathology. The pipeline comprised two cascaded CNN architectures: 3D CEL-UNet for segmentation and 3D Arthro-Net for threefold classification. A retrospective dataset of 571 CT scans featuring patients with various degrees of GH osteoarthritic-related pathologies was used to train, validate, and test the pipeline. Root mean squared error and Hausdorff distance median values for 3D reconstruction were 0.22 mm and 1.48 mm for the humerus and 0.24 mm and 1.48 mm for the scapula, outperforming state-of-the-art architectures and making it potentially suitable for a PSI-based shoulder arthroplasty preoperative plan context. The classification accuracy for OS, JS, and HSA consistently reached around 90% across all three categories. The computational time for the entire inference pipeline was less than 15 s, showcasing the framework's efficiency and compatibility with orthopedic radiology practice. The achieved reconstruction and classification accuracy, combined with the rapid processing time, represent a promising advancement towards the medical translation of artificial intelligence tools. This progress aims to streamline the preoperative planning pipeline, delivering high-quality bone surfaces and supporting surgeons in selecting the most suitable surgical approach according to the unique patient joint conditions.

Since the outbreak of the COVID-19 pandemic in 2019, medical imaging has emerged as a primary modality for diagnosing COVID-19 pneumonia. In clinical settings, the segmentation of lung infections from computed tomography images enables rapid and accurate quantification and diagnosis of COVID-19. Segmentation of COVID-19 infections in the lungs poses a formidable challenge, primarily due to the indistinct boundaries and limited contrast presented by ground glass opacity manifestations. Moreover, the confounding similarity among infiltrates, lung tissues, and lung walls further complicates this segmentation task. To address these challenges, this paper introduces a novel deep network architecture, called CAD-Unet, for segmenting COVID-19 lung infections. In this architecture, capsule networks are incorporated into the existing Unet framework. Capsule networks represent a novel type of network architecture that differs from traditional convolutional neural networks. They utilize vectors for information transfer among capsules, facilitating the extraction of intricate lesion spatial information. Additionally, we design a capsule encoder path and establish a coupling path between the unet encoder and the capsule encoder. This design maximizes the complementary advantages of both network structures while achieving efficient information fusion. Finally, extensive experiments are conducted on four publicly available datasets, encompassing binary segmentation tasks and multi-class segmentation tasks. The experimental results demonstrate the superior segmentation performance of the proposed model. The code has been released at: https://github.com/AmanoTooko-jie/CAD-Unet.

Revision total hip arthroplasty (rTHA) involving large acetabular defects is associated with high early failure rates, primarily due to cup loosening. Most acetabular defect classification systems used in surgical planning are based on planar radiographs and do not encapsulate three-dimensional geometry and morphology of the acetabular defect. This study aimed to develop an automated computational modeling pipeline for rapid generation of three-dimensional acetabular bone defect geometry. The framework employed artificial neural network segmentation of preoperative pelvic computed tomography (CT) images and statistical shape model generation for defect reconstruction in 60 rTHA patients. Regional acetabular absolute defect volumes (ADV), relative defect volumes (RDV) and defect depths (DD) were calculated and stratified within Paprosky classifications. Defect geometries from the automated modeling pipeline were validated against manually reconstructed models and were found to have a mean dice coefficient of 0.827 and a mean relative volume error of 16.4%. The mean ADV, RDV and DD of classification groups generally increased with defect severity. Except for superior RDV and ADV between 3A and 2A defects, and anterior RDV and DD between 3B and 3A defects, statistically significant differences in ADV, RDV or DD were only found between 3B and 2B-2C defects (p < 0.05). Poor correlations observed between ADV, RDV, and DD within Paprosky classifications suggest that quantitative measures are not unique to each Paprosky grade. The automated modeling tools developed may be useful in surgical planning and computational modeling of rTHA.

Deep learning-based medical image segmentation and surface mesh generation typically involve a sequential pipeline from image to segmentation to meshes, often requiring large training datasets while making limited use of prior geometric knowledge. This may lead to topological inconsistencies and suboptimal performance in low-data regimes. To address these challenges, we propose a data-efficient deep learning method for direct 3D anatomical object surface meshing using geometric priors. Our approach employs a multi-resolution graph neural network that operates on a prior geometric template which is deformed to fit object boundaries of interest. We show how different templates may be used for the different surface meshing targets, and introduce a novel masked autoencoder pretraining strategy for 3D spherical data. The proposed method outperforms nnUNet in a one-shot setting for segmentation of the pericardium, left ventricle (LV) cavity and the LV myocardium. Similarly, the method outperforms other lumen segmentation operating on multi-planar reformatted images. Results further indicate that mesh quality is on par with or improves upon marching cubes post-processing of voxel mask predictions, while remaining flexible in the choice of mesh triangulation prior, thus paving the way for more accurate and topologically consistent 3D medical object surface meshing.

This study aims to investigate the impact of adaptive statistical iterative reconstruction-Veo (ASIR-V) and deep learning image reconstruction (DLIR) algorithms on the quantification of pericoronary adipose tissue (PCAT) and epicardial adipose tissue (EAT). Furthermore, we propose to explore the feasibility of correcting the effects through fat threshold adjustment. A retrospective analysis was conducted on the imaging data of 134 patients who underwent coronary CT angiography (CCTA) between December 2023 and January 2024. These data were reconstructed into seven datasets using filtered back projection (FBP), ASIR-V at three different intensities (ASIR-V 30%, ASIR-V 50%, ASIR-V 70%), and DLIR at three different intensities (DLIR-L, DLIR-M, DLIR-H). Repeated-measures ANOVA was used to compare differences in fat, PCAT and EAT attenuation values among the reconstruction algorithms, and Bland-Altman plots were used to analyze the agreement between ASIR-V or DLIR and FBP algorithms in PCAT attenuation values. Compared to FBP, ASIR-V 30 %, ASIR-V 50 %, ASIR-V 70 %, DLIR-L, DLIR-M, and DLIR-H significantly increased fat attenuation values (-103.91 ± 12.99 HU, -102.53 ± 12.68 HU, -101.14 ± 12.78 HU, -101.81 ± 12.41 HU, -100.87 ± 12.25 HU, -99.08 ± 12.00 HU vs. -105.95 ± 13.01 HU, all p < 0.001). When the fat threshold was set at -190 to -30 HU, ASIR-V and DLIR algorithms significantly increased PCAT and EAT attenuation values compared to FBP algorithm (all p < 0.05), with these values increasing as the reconstruction intensity level increased. After correction with a fat threshold of -200 to -35 HU for ASIR-V 30 %, -200 to -40 HU for ASIR-V 50 % and DLIR-L, and -200 to -45 HU for ASIR-V 70 %, DLIR-M, and DLIR-H, the mean differences in PCAT attenuation values between ASIR-V or DLIR and FBP algorithms decreased (-0.03 to 1.68 HU vs. 2.35 to 8.69 HU), and no significant difference was found in PCAT attenuation values between FBP and ASIR-V 30 %, ASIR-V 50 %, ASIR-V 70 %, DLIR-L, and DLIR-M (all p > 0.05). Compared to the FBP algorithm, ASIR-V and DLIR algorithms increase PCAT and EAT attenuation values. Adjusting the fat threshold can mitigate the impact of ASIR-V and DLIR algorithms on PCAT attenuation values.

Lung cancer remains the leading cause of cancer-related mortality worldwide, with its early detection and effective treatment posing significant clinical challenges. Radiomics, the extraction of quantitative features from medical imaging, has emerged as a promising approach for enhancing diagnostic accuracy, predicting treatment responses, and personalising patient care. This review explores the role of radiomics in lung cancer diagnosis and management, with methods ranging from handcrafted radiomics to deep learning techniques that can capture biological intricacies. The key applications are highlighted across various stages of lung cancer care, including nodule detection, histology prediction, and disease staging, where artificial intelligence (AI) models demonstrate superior specificity and sensitivity. The article also examines future directions, emphasising the integration of large language models, explainable AI (XAI), and super-resolution imaging techniques as transformative developments. By merging diverse data sources and incorporating interpretability into AI models, radiomics stands poised to redefine clinical workflows, offering more robust and reliable tools for lung cancer diagnosis, treatment planning, and outcome prediction. These advancements underscore radiomics' potential in supporting precision oncology and improving patient outcomes through data-driven insights.

The recent popularity of foundation models and the pre-train-and-adapt paradigm, where a large-scale model is transferred to downstream tasks, is gaining attention for volumetric medical image segmentation. However, current transfer learning strategies devoted to full fine-tuning for transfer learning may require significant resources and yield sub-optimal results when the labeled data of the target task is scarce. This makes its applicability in real clinical settings challenging since these institutions are usually constrained on data and computational resources to develop proprietary solutions. To address this challenge, we formalize Few-Shot Efficient Fine-Tuning (FSEFT), a novel and realistic scenario for adapting medical image segmentation foundation models. This setting considers the key role of both data- and parameter-efficiency during adaptation. Building on a foundation model pre-trained on open-access CT organ segmentation sources, we propose leveraging Parameter-Efficient Fine-Tuning and black-box Adapters to address such challenges. Furthermore, novel efficient adaptation methodologies are introduced in this work, which include Spatial black-box Adapters that are more appropriate for dense prediction tasks and constrained transductive inference, leveraging task-specific prior knowledge. Our comprehensive transfer learning experiments confirm the suitability of foundation models in medical image segmentation and unveil the limitations of popular fine-tuning strategies in few-shot scenarios. The project code is available: https://github.com/jusiro/fewshot-finetuning.

To evaluate and compare the diagnostic performance of CT texture analysis (CTTA), perfusion CT (PCT), and dual-energy CT (DECT) in distinguishing between clear-cell renal cell carcinoma (ccRCC) and non-ccRCC. This retrospective study included 66 patients with RCC (52 ccRCC and 14 non-ccRCC) who underwent DECT and PCT imaging before surgery (2017-2022). This DECT parameters (iodine concentration, iodine ratio [IR]) and PCT parameters (blood flow, blood volume, mean transit time, time to peak) were measured using circular regions of interest (ROIs). CT texture analysis features were extracted from manually annotated corticomedullary-phase images. A machine learning (ML) model was developed to differentiate RCC subtypes, with performance evaluated using k-fold cross-validation. Multivariate logistic regression analysis was performed to assess the predictive value of each imaging modality. All 3 imaging modalities demonstrated high diagnostic accuracy, with F1 scores of 0.9107, 0.9358, and 0.9348 for PCT, DECT, and CTTA, respectively. None of the 3 models were significantly different (P > 0.05). While each modality could effectively differentiate between ccRCC and non-ccRCC, higher IR on DECT and increased entropy on CTTA were independent predictors of ccRCC, with F1 scores of 0.9345 and 0.9272, respectively (P < 0.001). Dual-energy CT achieved the highest individual performance, with IR being the best predictor (F1 = 0.902). Iodine ratio was significantly higher in ccRCC (65.12 ± 23.73) compared to non-ccRCC (35.17 ± 17.99, P < 0.001), yielding an Area under curve (AUC) of 0.91, sensitivity of 87.5%, and specificity of 89.3%. Entropy on CTTA was the strongest texture feature, with higher values in ccRCC (7.94 ± 0.336) than non-ccRCC (6.43 ± 0.297, P < 0.001), achieving an AUC of 0.94, sensitivity of 83.0%, and specificity of 92.3%. The combined ML model integrating DECT, PCT, and CTTA parameters yielded the highest diagnostic accuracy, with an F1 score of 0.954. PCT, DECT, and CTTA effectively differentiate RCC subtypes. However, IR (DECT) and entropy (CTTA) emerged as key independent markers, suggesting their clinical utility in RCC characterization. Accurate, non-invasive biomarkers are essential to differentiate RCC subtypes, aiding in prognosis and guiding targeted therapies, particularly in ccRCC, where treatment options differ significantly.