Early detection of the autosomal dominant polycystic kidney disease (ADPKD) is crucial as it is one of the most common causes of end-stage renal disease (ESRD) and kidney failure. The total kidney volume (TKV) can be used as a biomarker to quantify disease progression. The TKV calculation requires accurate delineation of kidney volumes, which is usually performed manually by an expert physician. However, this is time-consuming and automated segmentation is warranted. Furthermore, the scarcity of large annotated datasets hinders the development of deep learning solutions. In this work, we address this problem by implementing three attention mechanisms into the U-Net to improve TKV estimation. Additionally, we implement a cosine loss function that works well on image classification tasks with small datasets. Lastly, we apply a technique called sharpness aware minimization (SAM) that helps improve the generalizability of networks. Our results show significant improvements (p-value < 0.05) over the reference kidney segmentation U-Net. We show that the attention mechanisms and/or the cosine loss with SAM can achieve a dice score (DSC) of 0.918, a mean symmetric surface distance (MSSD) of 1.20 mm with the mean TKV difference of −1.72%, and R${}^2$ of 0.96 while using only 100 MRI datasets for training and testing. Furthermore, we tested four ensembles and obtained improvements over the best individual network, achieving a DSC and MSSD of 0.922 and 1.09 mm, respectively.

Life-threatening acute aortic dissection (AD) demands timely diagnosis for effective intervention. To streamline intrahospital workflows, automated detection of AD in abdominal computed tomography (CT) scans seems useful to assist humans. We aimed at creating a robust convolutional neural network (CNN)-based pipeline capable of real-time screening for signs of abdominal AD in CT. In this retrospective study, abdominal CT data from AD patients presenting with AD and from non-AD patients were collected (n 195, AD cases 94, mean age 65.9 years, female ratio 35.8%). A CNN-based algorithm was developed with the goal of enabling a robust, automated, and highly sensitive detection of abdominal AD. Two sets from internal (n = 32, AD cases 16) and external sources (n = 1189, AD cases 100) were procured for validation. The abdominal region was extracted, followed by the automatic isolation of the aorta region of interest (ROI) and highlighting of the membrane via edge extraction, followed by classification of the aortic ROI as dissected/healthy. A fivefold cross-validation was employed on the internal set, and an ensemble of the 5 trained models was used to predict the internal and external validation set. Evaluation metrics included receiver operating characteristic curve (AUC) and balanced accuracy. The AUC, balanced accuracy, and sensitivity scores of the internal dataset were 0.932 (CI 0.891–0.963), 0.860, and 0.885, respectively. For the internal validation dataset, the AUC, balanced accuracy, and sensitivity scores were 0.887 (CI 0.732–0.988), 0.781, and 0.875, respectively. Furthermore, for the external validation dataset, AUC, balanced accuracy, and sensitivity scores were 0.993 (CI 0.918–0.994), 0.933, and 1.000, respectively. The proposed automated pipeline could assist humans in expediting acute aortic dissection management when integrated into clinical workflows.

An accurate prognosis of renal function decline in Autosomal Dominant Polycystic Kidney Disease (ADPKD) is crucial for early intervention. Current biomarkers used are height-adjusted total kidney volume (HtTKV), estimated glomerular filtration rate (eGFR), and patient age. However, manually measuring kidney volume is time-consuming and subject to observer variability. Additionally, incorporating automatically generated features from kidney MRI images, along with conventional biomarkers, can enhance prognostic improvement. To address these issues, we developed two deep-learning algorithms. Firstly, an automated kidney volume segmentation model accurately calculates HtTKV. Secondly, we utilize segmented kidney volumes, predicted HtTKV, age, and baseline eGFR to predict chronic kidney disease (CKD) stages >=3A, >=3B, and a 30% decline in eGFR after 8 years from the baseline visit. Our approach combines a convolutional neural network (CNN) and a multi-layer perceptron (MLP). Our study included 135 subjects and the AUC scores obtained were 0.96, 0.96, and 0.95 for CKD stages >=3A, >=3B, and a 30% decline in eGFR, respectively. Furthermore, our algorithm achieved a Pearson correlation coefficient of 0.81 between predicted and measured eGFR decline. We extended our approach to predict distinct CKD stages after eight years with an AUC of 0.97. The proposed approach has the potential to enhance monitoring and facilitate prognosis in ADPKD patients, even in the early disease stages.

Objective . We present seg2med (segmentation-to-medical images), a modular framework for anatomy-driven multimodal medical image synthesis. The system integrates three components to enable high-fidelity, cross-modality generation of computed tomography (CT) and magnetic resonance (MR) images based on structured anatomical priors. Approach . First, anatomical maps are independently derived from three sources: real patient data, extended cardiac-torso (XCAT) digital phantoms, and anatomies-synthetic subjects created by combining organs from multiple patients. Second, we introduce PhysioSynth, a modality-specific simulator that converts anatomical masks into imaging-like prior volumes using tissue-dependent parameters (e.g. HU, T 1, T 2, ρ ) and modality-specific signal models. It supports simulation of CT and multiple MR sequences, including gradient-echo, SPACE, and volumetric interpolated breath-hold examination. Third, the synthesized anatomical priors are used to train 2-channel conditional denoising diffusion probabilistic models, which take the anatomical prior as a structural condition alongside the noisy image, enabling it to generate high-quality, structurally aligned images within its modality. Main results . The framework achieves a structural similarity index measure (SSIM) of 0.94 ± 0.02 for CT and 0.82 ± 0.12 for MR images compared to real patient data, and 0.78 ± 0.04 FSIM for simulated CT from XCAT. The generative quality is further supported by a Fréchet inception distance of 20.20 for CT synthesis. In modality conversion tasks, seg2med attains SSIM scores of 0.91 ± 0.03 (MR → CT) and 0.77 ± 0.04 (CT → MR). Significance . In anatomical fidelity evaluation, synthetic CT images achieve a mean Dice coefficient exceeding 0.90 for 11 key abdominal organs, and over 0.80 for 34 of 59 total organs. These results underscore seg2med’s utility in cross-modality image synthesis, dataset augmentation, and anatomy-aware AI development in medical imaging.

Introduction: Multiple sclerosis (MS) is a chronic neurological disorder characterized by the progressive loss of myelin and axonal structures in the central nervous system. Accurate detection and monitoring of MS-related changes in brain structures are crucial for disease management and treatment evaluation. We propose a deep learning algorithm for creating Voxel-Guided Morphometry (VGM) maps from longitudinal MRI brain volumes for analyzing MS disease activity. Our approach focuses on developing a generalizable model that can effectively be applied to unseen datasets.Methods: Longitudinal MS patient high-resolution 3D T1-weighted follow-up imaging from three different MRI systems were analyzed. We employed a 3D residual U-Net architecture with attention mechanisms. The U-Net serves as the backbone, enabling spatial feature extraction from MRI volumes. Attention mechanisms are integrated to enhance the model's ability to capture relevant information and highlight salient regions. Furthermore, we incorporate image normalization by histogram matching and resampling techniques to improve the networks' ability to generalize to unseen datasets from different MRI systems across imaging centers. This ensures robust performance across diverse data sources.Results: Numerous experiments were conducted using a dataset of 71 longitudinal MRI brain volumes of MS patients. Our approach demonstrated a significant improvement of 4.3% in mean absolute error (MAE) against the state-of-the-art (SOTA) method. Furthermore, the algorithm's generalizability was evaluated on two unseen datasets (n = 116) with an average improvement of 4.2% in MAE over the SOTA approach.Discussion: Results confirm that the proposed approach is fast and robust and has the potential for broader clinical applicability.

The prognosis of renal function decline in Autosomal Dominant Polycystic Kidney Disease (ADPKD) is vital for early intervention. Currently, the accepted biomarkers are height-adjusted total kidney volume (HtTKV) with estimated glomerular filtration rate (eGFR) and patient age. However, kidney volume delineation is time-consuming and prone to observer variability. Furthermore, improvement in prognosis can be achieved by incorporating automatically generated features of kidney MRI images in addition to the conventional biomarkers. Hence, to improve prediction we develop two deep learning algorithms. At first, we create an automated kidney volume segmentation model that can accurately calculate HtTKV. Secondly, we use the segmented kidney volumes with the predicted HtTKV, age, and eGFR at the baseline visit. Here, we use a combination of convolutional neural network (CNN) and multi-layer perceptron (MLP) for the prediction of chronic kidney disease (CKD) stages >=3A, >=3B, and a 30% decline in eGFR after 8 years from the baseline visit. We obtain AUC scores of 0.96, 0.96, and 0.95 for CKD stages >=3A, >=3B, and 30% decline in eGFR, respectively. Moreover, our algorithm achieves a Pearson correlation coefficient of 0.81 between predicted and measured eGFR decline. We further extend our approach to predict distinct CKD stages after eight years with high accuracy. The proposed approach might improve monitoring and support the prognosis of ADPKD patients from the earliest disease stages.