Objectives: Introducing SPINEPS, a deep learning method for semantic and instance segmentation of 14 spinal structures (ten vertebra substructures, intervertebral discs, spinal cord, spinal canal, and sacrum) in whole-body sagittal T2-weighted turbo spin echo images. Material and methods: This local ethics committee-approved study utilized a public dataset (train/test 179/39 subjects, 137 female), a German National Cohort (NAKO) subset (train/test 1412/65 subjects, mean age 53, 694 female), and an in-house dataset (test 10 subjects, mean age 70, 5 female). SPINEPS is a semantic segmentation model, followed by a sliding window approach utilizing a second model to create instance masks from the semantic ones. Segmentation evaluation metrics included the Dice score and average symmetrical surface distance (ASSD). Statistical significance was assessed using the Wilcoxon signed-rank test. Results: On the public dataset, SPINEPS outperformed a nnUNet baseline on every structure and metric (e.g., an average over vertebra instances: dice 0.933 vs 0.911, p < 0.001, ASSD 0.21 vs 0.435, p < 0.001). SPINEPS trained on automated annotations of the NAKO achieves an average global Dice score of 0.918 on the combined NAKO and in-house test split. Adding the training data from the public dataset outperforms this (average instance-wise Dice score over the vertebra substructures 0.803 vs 0.778, average global Dice score 0.931 vs 0.918). Conclusion: SPINEPS offers segmentation of 14 spinal structures in T2w sagittal images. It provides a semantic mask and an instance mask separating the vertebrae and intervertebral discs. This is the first publicly available algorithm to enable this segmentation. Key Points: Question No publicly available automatic approach can yield semantic and instance segmentation masks for the whole spine (including posterior elements) in T2-weighted sagittal TSE images. Findings Segmenting semantically first and then instance-wise outperforms a baseline trained directly on instance segmentation. The developed model produces high-resolution MRI segmentations for the whole spine. Clinical relevance This study introduces an automatic approach to whole spine segmentation, including posterior elements, in arbitrary fields of view T2w sagittal MR images, enabling easy biomarker extraction, automatic localization of pathologies and degenerative diseases, and quantifying analyses as downstream research.

Counterfactual explanations (CEs) aim to enhance the interpretability of machine learning models by illustrating how alterations in input features would affect the resulting predictions. Common CE approaches require an additional model and are typically constrained to binary counterfactuals. In contrast, we propose a novel method that operates directly on the latent space of a generative model, specifically a Diffusion Autoencoder (DAE). This approach offers inherent interpretability by enabling the generation of CEs and the continuous visualization of the model’s internal representation across decision boundaries. Our method leverages the DAE’s ability to encode images into a semantically rich latent space in an unsupervised manner, eliminating the need for labeled data or separate feature extraction models. We show that these latent representations are helpful for medical condition classification and the ordinal regression of severity pathologies, such as vertebral compression fractures (VCF) and diabetic retinopathy (DR). Beyond binary CEs, our method supports the visualization of ordinal CEs using a linear model, providing deeper insights into the model’s decision-making process and enhancing interpretability. Experiments across various medical imaging datasets demonstrate the method’s advantages in interpretability and versatility. The linear manifold of the DAE’s latent space allows for meaningful interpolation and manipulation, making it a powerful tool for exploring medical image properties. Our code is available at https://github.com/matanat/dae_counterfactual

Accurate calibration of finite element (FE) models is essential across various biomechanical applications, including human intervertebral discs (IVDs), to ensure their reliability and use in diagnosing and planning treatments. However, traditional calibration methods are computationally intensive, requiring iterative, derivative-free optimization algorithms that often take days to converge. This study addresses these challenges by introducing a novel, efficient, and effective calibration method demonstrated on a human L4-L5 IVD FE model as a case study using a neural network (NN) surrogate. The NN surrogate predicts simulation outcomes with high accuracy, outperforming other machine learning models, and significantly reduces the computational cost associated with traditional FE simulations. Next, a Projected Gradient Descent (PGD) approach guided by gradients of the NN surrogate is proposed to efficiently calibrate FE models. Our method explicitly enforces feasibility with a projection step, thus maintaining material bounds throughout the optimization process. The proposed method is evaluated against state-of-the-art Genetic Algorithm (GA) and inverse model baselines on synthetic and in vitro experimental datasets. Our approach demonstrates superior performance on synthetic data, achieving a Mean Absolute Error (MAE) of 0.06 compared to the baselines' MAE of 0.18 and 0.54, respectively. On experimental specimens, our method outperforms the baseline in 5 out of 6 cases. While our approach requires initial dataset generation and surrogate training, these steps are performed only once, and the actual calibration takes under three seconds. In contrast, traditional calibration time scales linearly with the number of specimens, taking up to 8 days in the worst-case. Such efficiency paves the way for applying more complex FE models, potentially extending beyond IVDs, and enabling accurate patient-specific simulations.

Chronic rhinosinusitis (CRS) is a common and persistent sinus inflammation that affects 5%-12% of the general population. It substantially reduces quality of life, yet its severity is often challenging to assess objectively. The Lund-Mackay score (LMS) rates sinus opacification but is typically assessed manually and subjectively. We introduce Paranasal Segmentation for Imaging-based Disease Evaluation (PARASIDE), an automatic tool for segmenting air and soft tissue volumes of the structures of the sinus maxillaris, frontalis, sphenoidalis, and ethmoidalis in T1-weighted magnetic resonance imaging. Utilizing that segmentation, we quantify feature relations such as volume, thickness, and intensity relations which were previously observed only manually and subjectively. Using these features, we regress the Total Lund-Mackay Score (TLMS) of each subject. We compare our approach against established baselines: the Quantitative Opacification Score (QOS) and the Quantitative Lund-Mackay Score (QLMS). PARASIDE achieves a mean-squared error (MSE) of 2.444 and mean absolute error (MAE) of 1.181 for TLMS prediction, outperforming the QOS/QLMS baseline (MSE = 3.784, MAE = 1.445). The segmentation achieves a mean Dice similarity coefficient of 0.882 ± 0.138 and an average symmetric surface distance (ASSD) of 0.311 ± 0.354 mm across all structures. PARASIDE enables the first automated whole-paranasal sinus segmentation for T1-weighted MRI, extracting quantitative features that predict CRS severity more accurately than existing volumetric scoring methods. By integrating high-quality segmentation with fully automated TLMS estimation, our system offers a reproducible and objective assessment tool in clinical workflows, with the potential to reduce inter-rater variability, accelerate reporting, and support large-scale retrospective studies.

Purpose: The correct identification of the thoracolumbar junction is mandatory for labeling of the lumbar spine, particularly if visualized incompletely. The current state-of-the-art relies on a continuous counting-based (CCBC), a rib-length-based (RLBC), or rib-based classification (RBC) of the spine enumeration. However, the CCBC and RBC often lead to inconsistent labels depending on the visualized spine segments. Methods: In this retrospective study, three classifications for assessing the thoracolumbar junction were evaluated in CT scans from 2005 to 2018, covering the complete thoracolumbar spine: (1) CCBC, (2) RLBC/RBC, and (3) a vertebra-shape-based classification (VSBC). The classifications were compared regarding consistent labels depending on the visualized spine segments, agreement with nerve morphology, and inter-rater reliability. Results: CT-imaging data from 1,242 subjects (mean age, 63 years ± 12; 771 women) were included. The VSBC led to more consistent labels than all other classifications. Only the VSBC perfectly complied with the nerve morphology (VSBC, 100%; RBC, 97.2%, RLBC, 96.3%; CCBC, 92.6%). The VSBC had slightly superior inter-rater reliability than the RBC (0.61 vs. 0.59). Conclusion: The VSBC performs more reliably and consistently than other classifications in labeling the thoracolumbar junction, even for anatomical variants or incompletely visualized spines. Compared with rib-based classifications, the VSBC demonstrated modest but clear advantages in consistency and agreement with nerve morphology.