An Augmented Backward-Corrected Projector Splitting Integrator for Dynamical Low-Rank Training

Layer factorization has emerged as a widely used technique for training memory-efficient neural networks. However, layer factorization methods face several challenges, particularly a lack of robustness during the training process. To overcome this limitation, dynamical low-rank training methods have been developed, utilizing robust time integration techniques for low-rank matrix differential equations. Although these approaches facilitate efficient training, they still depend on computationally intensive QR and singular value decompositions of matrices with small rank. In this work, we introduce a novel low-rank training method that reduces the number of required QR decompositions. Our approach integrates an augmentation step into a projector-splitting scheme, ensuring convergence to a locally optimal solution. We provide a rigorous theoretical analysis of the proposed method and demonstrate its effectiveness across multiple benchmarks.

TotalSegmentator MRI: Sequence-Independent Segmentation of 59 Anatomical Structures in MR images

Purpose: To develop an open-source and easy-to-use segmentation model that can automatically and robustly segment most major anatomical structures in MR images independently of the MR sequence. Materials and Methods: In this study we extended the capabilities of TotalSegmentator to MR images. 298 MR scans and 227 CT scans were used to segment 59 anatomical structures (20 organs, 18 bones, 11 muscles, 7 vessels, 3 tissue types) relevant for use cases such as organ volumetry, disease characterization, and surgical planning. The MR and CT images were randomly sampled from routine clinical studies and thus represent a real-world dataset (different ages, pathologies, scanners, body parts, sequences, contrasts, echo times, repetition times, field strengths, slice thicknesses and sites). We trained an nnU-Net segmentation algorithm on this dataset and calculated Dice similarity coefficients (Dice) to evaluate the model's performance. Results: The model showed a Dice score of 0.824 (CI: 0.801, 0.842) on the test set, which included a wide range of clinical data with major pathologies. The model significantly outperformed two other publicly available segmentation models (Dice score, 0.824 versus 0.762; p<0.001 and 0.762 versus 0.542; p<0.001). On the CT image test set of the original TotalSegmentator paper it almost matches the performance of the original TotalSegmentator (Dice score, 0.960 versus 0.970; p<0.001). Conclusion: Our proposed model extends the capabilities of TotalSegmentator to MR images. The annotated dataset (https://zenodo.org/doi/10.5281/zenodo.11367004) and open-source toolkit (https://www.github.com/wasserth/TotalSegmentator) are publicly available.

Towards Unifying Anatomy Segmentation: Automated Generation of a Full-body CT Dataset via Knowledge Aggregation and Anatomical Guidelines

In this study, we present a method for generating automated anatomy segmentation datasets using a sequential process that involves nnU-Net-based pseudo-labeling and anatomy-guided pseudo-label refinement. By combining various fragmented knowledge bases, we generate a dataset of whole-body CT scans with $142$ voxel-level labels for 533 volumes providing comprehensive anatomical coverage which experts have approved. Our proposed procedure does not rely on manual annotation during the label aggregation stage. We examine its plausibility and usefulness using three complementary checks: Human expert evaluation which approved the dataset, a Deep Learning usefulness benchmark on the BTCV dataset in which we achieve 85% dice score without using its training dataset, and medical validity checks. This evaluation procedure combines scalable automated checks with labor-intensive high-quality expert checks. Besides the dataset, we release our trained unified anatomical segmentation model capable of predicting $142$ anatomical structures on CT data.