SV-Cine: Diagnosis-Conditioned Segmentation of Single Ventricle Physiology via Generative Data Augmentation

Single Ventricle Physiology (SVP) is a rare subtype of congenital heart disease characterized by the presence of a single functional cardiac ventricle with atypical anatomic configurations that challenge conventional image segmentation approaches. The scarcity of clinical data and the morphological diversity across SVP subtypes make the development of robust segmentation methods particularly difficult.

To address these limitations, the authors propose a cardiac MRI segmentation framework focused on ventricular chambers and myocardium segmentation tailored for SVP. This framework involves two main components:

1. A data augmentation pipeline that generates synthetic 3D cardiac meshes using SDF4CHD and corresponding synthetic cardiac MRI through generative modeling.

2. SV-Cine, which is introduced as "a diagnosis-conditioned adaptation of the foundation model CineMA that incorporates patient-level diagnostic information through Feature-wise Linear Modulation layers, enabling diagnosis-aware feature adaptation during segmentation."

The authors evaluated the framework on an internal cohort with varying SVP subtypes. SV-Cine "achieved median Dice scores of 0.89 (IQR: 0.80–0.91) for the left ventricle and 0.72 (IQR: 0.54–0.84) for the right ventricle, outperforming the strongest baseline, nnU-Net, by 0.39 Dice points on right ventricle segmentation. Furthermore, it yield[s] a median ejection fraction error of 5.55 percentage points (IQR: 3.41–7.69) for the dominant ventricle. The authors also noted that LV and myocardium segmentation performance was lower for the external cohort; whereas RV Dice scores were comparable for both cohorts."

The introduction highlights several key contributions:

(First contribution)

"the incorporation of minimal diagnosis information as a clinical prior to guide the segmentation network. Unlike most existing approaches that rely solely on image appearance, SV-Cine exploits the diagnosis to help resolve anatomical ambiguities. This is particularly relevant in SVP, where ventricular identity cannot always be inferred from image appearance alone because ventricular morphology, size, and spatial organization vary substantially across patients."

(Second contribution)

"the proposed synthetic data generation pipeline, which increases anatomical diversity beyond what can be achieved through conventional image augmentation by generating anatomically plausible cardiac anatomies corresponding to specific SVP subtypes. Notably, the entire framework was developed using only 11 real single-ventricle anatomies (10 from the publicly available HVSMR dataset and one from the original SDF4CHD training dataset)."

The originality of the proposed framework lies in "the combination of these two components. Diagnosis information is exploited consistently throughout the entire pipeline: it first guides the generation of diagnosis-conditioned synthetic anatomies and is then reused to condition the segmentation network. This unified use ensures consistency between data generation and downstream segmentation. Finally, this work suggests that a pretrained foundation model can be adapted to a highly specialized downstream task while still leveraging the anatomical knowledge learned from large-scale cardiac MRI datasets during pretraining."

The authors conclude that SV-Cine achieved the best overall balance among the evaluated methods, noting that Diagnosis conditioning had the largest impact on performance, particularly on right ventricular segmentation and functional estimation. The study suggests that combining diagnosis-informed modeling with anatomically guided synthetic data generation has the potential to improve ventricular segmentation and functional quantification in complex SVP anatomies.

The paper notes limitations, including the need for Larger, multi-center studies with external validation will be necessary to establish the generalizability and clinical utility of the proposed approach, and that the range of SVP subtypes that can be generated is constrained by the diagnoses present in the original training set. It also points out that the segmentation accuracy metrics were lowest for the RV in patients with a severely hypoplastic right ventricle, where the structure occupies very few pixels. Furthermore, it suggests extending the framework to a 4D formulation using temporal attention to improve consistency across cardiac cycles.

In summary, SV-Cine is a diagnosis-conditioned segmentation framework that utilizes generative data augmentation and foundation model adaptation to improve ventricular chamber and myocardium segmentation for SVP by incorporating patient diagnostic information into the network via Feature-wise Linear Modulation layers. This approach demonstrates superior performance compared to baseline methods like nnU-Net and CineMA when evaluated on internal cohorts, suggesting a feasible direction for automated quantitative analysis of SVP cardiac MRI. The framework's strength lies in its unified use of clinical priors across both synthetic data generation and segmentation conditioning.

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Summary:

Single Ventricle Physiology (SVP) is characterized by the presence of a single functional cardiac ventricle with atypical anatomic configurations that challenge conventional image segmentation approaches. Due to the scarcity of clinical data and morphological diversity across SVP subtypes, robust segmentation methods are difficult to develop. To address this, the authors propose a cardiac MRI segmentation framework called SV-Cine, which focuses on ventricular chambers and myocardium segmentation tailored for SVP. The framework consists of two main parts: first, a data augmentation pipeline that generates synthetic 3D cardiac meshes using SDF4CHD and corresponding synthetic cardiac MRI through generative modeling, and second, SV-Cine itself, described as "a diagnosis-conditioned adaptation of the foundation model CineMA that incorporates patient-level diagnostic information through Feature-wise Linear Modulation layers, enabling diagnosis-aware feature adaptation during segmentation."

The authors evaluated this framework on an internal cohort with varying SVP subtypes. The results showed that SV-Cine "achieved median Dice scores of 0.89 (IQR: 0.80–0.91) for the left ventricle and 0.72 (IQR: 0.54–0.84) for the right ventricle, outperforming the strongest baseline, nnU-Net, by 0.39 Dice points on right ventricle segmentation. It also yield[s] a median ejection fraction error of 5.55 percentage points (IQR: 3.41–7.69) for the dominant ventricle."

The methodology involves leveraging existing foundation models and synthetic data generation to increase anatomical diversity. The synthetic data pipeline uses the SDF4CHD model to generate anatomically diverse 3D cardiac geometries using a retrained SDF4CHD network, which learns a disentangled representation of cardiac anatomy by separating pathology-specific and patient-specific factors. These generated 3D anatomies are then converted into realistic images via a cGAN, where anatomical labels are injected through SPADE layers, incorporating contextual labels corresponding to surrounding structures for improved realism.

SV-Cine's segmentation network is modified from CineMA 11 by deriving a simplified clinical representation from the patient diagnosis, which takes the form of a binary vector d indicating the presence or absence of an HLV, HRV, and anatomically univentricular morphology. This diagnostic vector is encoded into a latent embedding, and this embedding is then used to condition the decoder through FiLM layers: FiLM (h) = γ × h + β where h denotes intermediate decoder features, and γ and β are learned modulation parameters conditioned on the diagnostic prior.

The authors compare SV-Cine against several baselines. SV-Cine achieved the highest Dice scores and lowest functional estimation errors among the evaluated methods. Specifically, compared to 2D nnU-Net, it showed higher Dice scores for all cardiac structures, with a largest improvement observed for RV segmentation. Compared to CineMA trained on MnM2, SV-Cine significantly improved RV Dice scores. The ablation study confirms that Diagnosis conditioning had the largest impact on performance, as removing it reduced the median RV Dice score from 0.72 [0.54–0.84] to 0.14 [0.00–0.4].

The framework's originality is highlighted by its unified use of clinical priors: Diagnosis information is exploited consistently throughout the entire pipeline: it first guides the generation of diagnosis-conditioned synthetic anatomies and is then reused to condition the segmentation network. The study concludes that SV-Cine provides a better compromise between detecting small ventricular structures and avoiding anatomically inconsistent segmentations, resulting in lower functional estimation errors. Limitations include the reliance on a relatively small cohort, the potential for synthesized images to differ visibly from real cine MRI, and the constraint of generated subtypes by the original training set diagnoses. Future work is suggested to extend this framework to a 4D formulation using temporal attention.

Keywords:

Congenital heart disease; Cardiac MRI; Segmentation; Foundation model adaptation; Diagnosis conditioning; Data augmentation; Single ventricle physiology.

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Single Ventricle Physiology (SVP) is characterized by the presence of a single functional cardiac ventricle with atypical anatomic configurations that challenge conventional image segmentation approaches. The scarcity of clinical data and the morphological diversity across SVP subtypes make the development of robust segmentation methods particularly difficult.

To address these limitations, the authors propose a cardiac MRI segmentation framework focused on ventricular chambers and myocardium segmentation tailored for SVP. First, we introduce a data augmentation pipeline that generates synthetic 3D cardiac meshes using SDF4CHD and corresponding synthetic cardiac MRI through generative modeling. Second, we introduce SV-Cine, "a diagnosis-conditioned adaptation of the foundation model CineMA that incorporates patient-level diagnostic information through Feature-wise Linear Modulation layers, enabling diagnosis-aware feature adaptation during segmentation."

We evaluated the framework on an internal cohort with varying SVP subtypes. SV-Cine "achieved median Dice scores of 0.89 (IQR: 0.80–0.91) for the left ventricle and 0.72 (IQR: 0.54–0.84) for the right ventricle, outperforming the strongest baseline, nnU-Net, by 0.39 Dice points on right ventricle segmentation. It also yield[s] a median ejection fraction error of 5.55 percentage points (IQR: 3.41–7.69) for the dominant ventricle."

The introduction highlights several key contributions: First, the incorporation of minimal diagnosis information as a clinical prior to guide the segmentation network. This exploits the diagnosis to help resolve anatomical ambiguities, which is relevant in SVP where ventricular identity cannot always be inferred from image appearance alone because ventricular morphology, size, and spatial organization vary substantially across patients. Second, the proposed synthetic data generation pipeline, which increases anatomical diversity by generating anatomically plausible cardiac anatomies corresponding to specific SVP subtypes.

The originality of the proposed framework lies in "the combination of these two components. Diagnosis information is exploited consistently throughout the entire pipeline: it first guides the generation of diagnosis-conditioned synthetic anatomies and is then reused to condition the segmentation network. This unified use ensures consistency between data generation and downstream segmentation. Finally, this work suggests that a pretrained foundation model can be adapted to a highly specialized downstream task while still leveraging the anatomical knowledge learned from large-scale cardiac MRI datasets during pretraining."

SV-Cine was evaluated directly on retrospective clinical cine MRI acquired at UCLA Health without any additional fine-tuning or domain adaptation. It achieved stable segmentation performance across the evaluated cohort despite the anatomical variability associated with different SVP subtypes.

Compared with all baseline methods, SV-Cine achieved the best overall balance among the evaluated methods. The largest improvements were observed for right ventricular segmentation, while functional estimation remained more challenging for severely hypoplastic or atretic ventricles because these structures occupy only a few pixels and are therefore highly sensitive to small segmentation errors.

Ablation experiments confirmed the contributions of each component: Diagnosis conditioning had the largest impact on performance, particularly on right ventricular segmentation and functional estimation. Synthetic data augmentation consistently improved robustness by increasing the anatomical diversity available during training, while foundation model pretraining further enhanced segmentation accuracy and reduced inter-subject variability.

Several limitations should be noted. The study was conducted on a relatively small, multi-center cohort of 39 patients. Although anatomically diverse, this cohort does not cover the full spectrum of SVP variability, and validation on larger, more diverse cohorts will be needed to confirm generalizability.

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**The originality of the proposed framework lies in "the combination of these two components. Diagnosis information is exploited consistently throughout the entire

Here are specific, actionable improvements for AI systems based on the SV-Cine framework:

1. The core improvement is the development of a segmentation model (SV-Cine) that incorporates explicit clinical diagnosis as a conditioning prior via Feature-wise Linear Modulation (FiLM) layers.

2. The improved AI system can perform highly accurate, structure-specific segmentation of cardiac MRI scans in Single Ventricle Physiology (SVP) patients, specifically:

@ - A specialized segmentation network trained on a foundation model architecture (CineMA backbone), which is conditioned by a low-dimensional diagnosis vector summarizing the patient's SVP subtype (HLV presence, HRV presence, univentricular morphology).

3. This system can reliably distinguish between dominant and hypoplastic ventricular structures (Left Ventricle vs. Right Ventricle) with significantly higher accuracy than baseline methods (e.g., nnU-Net or 2D U-Net), evidenced by superior Dice scores, especially for the often challenging right ventricle segmentation in SVP cases.

4. The system can provide more accurate quantitative functional assessments, specifically estimating Ejection Fraction (EF) and ventricular volumes with reduced error compared to standard models, particularly for hypoplastic ventricles where these structures are small and sensitive to segmentation errors.

5. The framework benefits from a robust synthetic data generation pipeline that creates anatomically diverse 3D cardiac meshes (using SDF4CHD) and corresponding realistic 2D short-axis MRI images (using a cGAN).

@ - This synthetic data allows the AI system to be trained effectively on rare SVP subtypes, overcoming the critical limitation of scarce clinical annotation data.

6. The improved AI system is capable of generalizing across different anatomical variations within SVP by leveraging both pretraining knowledge from large-scale datasets and diagnosis-specific conditioning from the synthetic training set.

7. The proposed method can be used to create a unified pipeline where clinical diagnosis guides both the generation of synthetic training data and the adaptation of the segmentation network, ensuring consistency between data augmentation and downstream task performance.