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.

To compare clinical ratings and signal-to-noise ratio (SNR) measures of a commercially available Deep Learning-based MRI reconstruction method (T2_(DR)) against conventional T2- turbo spin echo brain MRI (T2_(CN)). 100 consecutive patients with various neurological conditions underwent both T2_(DR) and T2_(CN) on a Siemens Vida 3 T scanner with a 64-channel head coil in the same examination. Acquisition times were 3.33 min for T2_(CN) and 1.04 min for T2_(DR). Four neuroradiologists evaluated overall image quality (OIQ), diagnostic safety (DS), and image artifacts (IA), blinded to the acquisition mode. SNR and SNR_eff (adjusted for acquisition time) were calculated for air, grey- and white matter, and cerebrospinal fluid. The mean patient age was 43.6 years (SD 20.3), with 54 females. The distribution of non-diagnostic ratings did not differ significantly between T2_(CN) and T2_(DR) (IA p = 0.108; OIQ: p = 0.700 and DS: p = 0.652). However, when considering the full spectrum of ratings, significant differences favouring T2_(CN) emerged in OIQ (p = 0.003) and IA (p < 0.001). T2_(CN) had higher SNR (157.9, SD 123.4) than T2_(DR) (112.8, SD 82.7), p < 0.001, but T2_(DR) demonstrated superior SNR_eff (14.1, SD 10.3) compared to T2_(CN) (10.8, SD 8.5), p < 0.001. Our results suggest that while T2_(DR) may be clinically applicable for a diagnostic setting, it does not fully match the quality of high-standard conventional T2_(CN), MRI acquisitions.

To review the current risk and prognostic stratification systems in localised prostate cancer. To explore some of the most promising adjuncts to clinical models and what the evidence has shown regarding their value. There are many new biomarker-based models seeking to improve, optimise or replace clinical models. There are promising data on the value of MRI, radiomics, genomic classifiers and most recently artificial intelligence tools in refining stratification. Despite the extensive literature however, there remains uncertainty on where in pathways they can provide the most benefit and whether a biomarker is most useful for prognosis or predictive use. Comparisons studies have also often overlooked the fact that clinical models have themselves evolved and the context of the baseline used in biomarker studies that have shown superiority have to be considered. For new biomarkers to be included in stratification models, well designed prospective clinical trials are needed. Until then, there needs to be caution in interpretation of their use for day-to-day decision making. It is critical that users balance any purported incremental value against the performance of the latest clinical classification and multivariate models especially as the latter are cost free and widely available.

Pathologic complete response (pCR) following neoadjuvant chemotherapy (NACT) is a critical prognostic marker for patients with breast cancer, potentially allowing surgery omission. However, noninvasive and accurate pCR diagnosis remains a significant challenge due to the limitations of current imaging techniques, particularly in cases where tumors completely disappear post-NACT. We developed a novel framework incorporating Dimensional Accumulation for Layered Images (DALI) and an Attention-Box annotation tool to address the unique challenge of analyzing imaging data where target lesions are absent. These methods transform three-dimensional magnetic resonance imaging into two-dimensional representations and ensure consistent target tracking across time-points. Preprocessing techniques, including tissue-region normalization and subtraction imaging, were used to enhance model performance. Imaging features were extracted using radiomics and pretrained deep-learning models, and machine-learning algorithms were integrated into a stacked ensemble model. The approach was developed using the I-SPY 2 dataset and validated with an independent Tangshan People's Hospital cohort. The stacked ensemble model achieved superior diagnostic performance, with an area under the receiver operating characteristic curve of 0.831 (95 % confidence interval, 0.769-0.887) on the test set, outperforming individual models. Tissue-region normalization and subtraction imaging significantly enhanced diagnostic accuracy. SHAP analysis identified variables that contributed to the model predictions, ensuring model interpretability. This innovative framework addresses challenges of noninvasive pCR diagnosis. Integrating advanced preprocessing techniques improves feature quality and model performance, supporting clinicians in identifying patients who can safely omit surgery. This innovation reduces unnecessary treatments and improves quality of life for patients with breast cancer.

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.

To evaluate the role of multimodal magnetic resonance imaging radiomics features in predicting early recurrence of primary central nervous system lymphoma (PCNSL) and to investigate their correlation with patient prognosis. A retrospective analysis was conducted on 145 patients with PCNSL who were treated with high-dose methotrexate-based chemotherapy. Clinical data and MRI images were collected, with tumor regions segmented using ITK-SNAP software. Radiomics features were extracted via Pyradiomics, and predictive models were developed using various machine learning algorithms. The predictive performance of these models was assessed using receiver operating characteristic (ROC) curves. Additionally, Cox regression analysis was employed to identify risk factors associated with progression-free survival (PFS). In the cohort of 145 PCNSL patients (72 recurrence, 73 non-recurrence), clinical characteristics were comparable between groups except for multiple lesion frequency (61.1% vs. 39.7%, p < 0.05) and not receiving consolidation therapy (44.4% vs. 13.7%, p < 0.05). A total of 2392 radiomics features were extracted from CET1 and T2WI MRI sequence. Combining clinical variables, 10 features were retained after the feature selection process. The logistic regression (LR) model exhibited superior predictive performance in the test set to predict PCNSL early relapse, with an area under the curve (AUC) of 0.887 (95 % confidence interval: 0.785-0.988). Multivariate Cox regression identified the Cli-Rad score as an independent prognostic factor for PFS. Significant difference in PFS was observed between high- and low-risk groups defined by Cli-Rad score (8.24 months vs. 24.17 months, p < 0.001). The LR model based on multimodal MRI radiomics and clinical features, can effectively predict early recurrence of PCNSL, while the Cli-Rad score could independently forecast PFS among PCNSL patients.

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.

Although there are relatively few diverse, high-quality medical imaging datasets on which to train computer vision artificial intelligence models, even fewer datasets contain expertly classified observations that can be repurposed to train or test such models. The traditional annotation process is laborious and time-consuming. Repurposing annotations and consolidating similar types of annotations from disparate sources has never been practical. Until recently, the use of natural language processing to convert a clinical radiology report into labels required custom training of a language model for each use case. Newer technologies such as large language models have made it possible to generate accurate and normalized labels at scale, using only clinical reports and specific prompt engineering. The combination of automatically generated labels extracted and normalized from reports in conjunction with foundational image models provides a means to create labels for model training. This article provides a short history and review of the annotation and labeling process of medical images, from the traditional manual methods to the newest semiautomated methods that provide a more scalable solution for creating useful models more efficiently. <b>Keywords:</b> Feature Detection, Diagnosis, Semi-supervised Learning © RSNA, 2025.

Artificial intelligence (AI) may provide a solution for improving access to expert, timely, and accurate magnetic resonance imaging (MRI) interpretation. The PARADIGM trial will provide level 1 evidence on the role of AI in the diagnosis of prostate cancer on MRI.

Precise segmentation and uncertainty estimation are crucial for error identification and correction in medical diagnostic assistance. Existing methods mainly rely on pixel-wise uncertainty estimations. They (1) neglect the global context, leading to erroneous uncertainty indications, and (2) bring attention interference, resulting in the waste of extensive details and potential understanding confusion. In this paper, we propose a novel structural uncertainty estimation method, based on Convolutional Neural Networks (CNN) and Active Shape Models (ASM), named SU-ASM, which incorporates global shape information for providing precise segmentation and uncertainty estimation. The SU-ASM consists of three components. Firstly, multi-task generation provides multiple outcomes to assist ASM initialization and shape optimization via a multi-task learning module. Secondly, information fusion involves the creation of a Combined Boundary Probability (CBP) and along with a rapid shape initialization algorithm, Key Landmark Template Matching (KLTM), to enhance boundary reliability and select appropriate shape templates. Finally, shape model fitting where multiple shape templates are matched to the CBP while maintaining their intrinsic shape characteristics. Fitted shapes generate segmentation results and structural uncertainty estimations. The SU-ASM has been validated on cardiac ultrasound dataset, ciliary muscle dataset of the anterior eye segment, and the chest X-ray dataset. It outperforms state-of-the-art methods in terms of segmentation and uncertainty estimation.