Anatomical landmark segmentation serves as a critical initial step for robust multimodal registration during computer-assisted interventions. Current approaches predominantly rely on deep learning, which often necessitates the extensive manual generation of annotated datasets. In this paper, we present a novel strategy for creating large annotated datasets using a statistical shape model (SSM) based on a mean shape that is manually labeled only once. We demonstrate the method's efficacy through its application to deep-learning-based anatomical landmark segmentation, specifically targeting the detection of the anterior ridge and the falciform ligament in 3D liver shapes. A specialized deep learning network was trained with 8,800 annotated liver shapes generated by the SSM. The network's performance was evaluated on 500 unseen synthetic SSM shapes, yielding a mean Intersection over Union of 91.4% (87.4% for the anterior ridge and 87.6% for the falciform ligament). Subsequently, the network was applied to clinical patient liver shapes, with qualitative evaluation indicating promising results and highlighting the generalizability of the proposed approach. Our findings suggest that the SSM-based data generation approach alleviates the labor-intensive process of manual labeling while enabling the creation of large annotated training datasets for machine learning. Although our study focuses on liver anatomy, the proposed methodology holds potential for a broad range of applications where annotated training datasets play a pivotal role in developing accurate deep-learning models.
Figures & tables
Figure 1: Overview of the proposed method employing an SSM for data generation. The mean shape derived from the SSM is manually labeled once (in this example: red: anterior ridge, blue: falciform ligament). Subsequently, labels are transferred to generated shapes using vertex indices. The annotated dataset is then utilized for training a deep-learning network.
Figure 2: Two examples illustrating the aggregated labels annotated by the four individuals (first and third shape) and the corresponding results from the label transfer from the mean shape (second and last shape).
Figure 3: Evaluation of three examples from the synthetic SSM test dataset. The anterior ridge prediction is shown in blue and the falciform ligament in red. The last two rows show the mIoU over all labels as well as the IoU for the anterior ridge and the falciform ligament.
Figure 4: Qualitative evaluation on six examples from the MedShapeNet dataset [ 18 ] . The anterior ridge prediction is shown in blue and the falciform ligament in red.
Deep learning and generative models are advancing rapidly, with synthetic data increasingly being integrated into training pipelines for downstream analysis tasks. However, in medical imaging, their adoption remains constrained by the scarcity of reliable annotated datasets. To address this limitation, we propose 3D-LLDM, a label-guided 3D latent diffusion model that generates high-quality synthetic magnetic resonance (MR) volumes with corresponding anatomical segmentation masks. Our approach uses hepatobiliary phase MR images enhanced with the Gd-EOB-DTPA contrast agent to derive structural masks for the liver, portal vein, hepatic vein, and hepatocellular carcinoma, which then guide volumetric synthesis through a ControlNet-based architecture. Trained on 720 real clinical hepatobiliary phase MR scans from Samsung Medical Center, 3D-LLDM achieves a Fréchet Inception Distance (FID) of 28.31, improving over GANs by 70.9% and over state-of-the-art diffusion baselines by 26.7%. When used for data augmentation, the synthetic volumes improve hepatocellular carcinoma segmentation by up to 11.153% Dice score across five CNN architectures.
Kyeonghun Kim, Jaehyeok Bae, Youngung Han +10
OUTTA, Republic of Korea · Stanford University, USA · Seoul National University, Republic of Korea +6
Statistical shape modeling (SSM) is central to population level analysis of anatomical variability, yet most existing approaches rely on densely annotated segmentations and fixed latent representations. These requirements limit scalability and reduce flexibility when modeling complex anatomical variation. We introduce MorphoFlow, a sparse supervised generative shape modeling framework that learns compact probabilistic shape representations directly from sparse surface annotations. MorphoFlow integrates neural implicit shape representations with an autodecoder formulation and autoregressive normalizing flows to learn an expressive probabilistic density over the latent shape space. The neural implicit representation enables resolution-agnostic modeling of 3D anatomy, while the autodecoder formulation supports direct optimization of per-instance latent codes under sparse supervision. The autoregressive flow captures the distribution of latent anatomical variability providing a tractable, likelihood-based generative model of shapes. To promote compact and structured latent representations, we incorporate adaptive latent relevance weighting through sparsity-inducing priors, enabling the model to regulate the contribution of individual latent dimensions according to their relevance to the underlying anatomical variation while preserving generative expressivity. The resulting latent space supports uncertainty quantification and anatomically plausible shape synthesis without manual latent dimensionality tuning. Evaluation on publicly available lumbar vertebrae and femur datasets demonstrates accurate high-resolution reconstruction from sparse inputs and recovery of structured modes of anatomical variation consistent with population level trends.
Mokshagna Sai Teja Karanam, Tushar Kataria, Shireen Elhabian
Kahlert School of Computing, Scientific Computing and Imaging Institute, University of Utah, Utah, USA
Current state-of-the-art segmentation models still produce anatomically implausible shapes, such as broken vessels and fragmented glands, on the structures that are hardest to segment. Learned shape representations capture what structures look like and can help correct these errors, but most cover a fixed set of anatomical classes. Extending this coverage across classes and imaging modalities is costly because it often requires new voxel-level annotations. We propose Diffusion for Anatomical Reshaping with Text (DART), which learns a shared shape representation from segmentation probabilities. It uses anatomical descriptions to correct these probabilities without receiving medical images. Across 13 public benchmarks, absolute Dice gains reach 31% for individual gallbladder cases in CT and 10% for individual prostate cases in MRI. Using existing segmentation predictions, we scale a single shape representation from 50 to 377 anatomical classes without new voxel-level annotations for correction training. The shared representation also corrects four lesion classes absent from training and transfers from CT to MRI without retraining. Corrected labels also improve segmentation models, raising one model's Dice from 57.1% to 67.5%. The same pipeline supports annotations for 45,000 CT scans. These results suggest that sharing shape information within and across classes can reduce the need for new voxel-level annotations when extending segmentation to more anatomical classes and imaging modalities. The model is available at https://huggingface.co/Koushik45/DART-Seg
Koushik Swarna, Pedro R. A. S. Bassi, Lam Ngo +4
Johns Hopkins University · ELLIS Institute Finland · Aalto University +1