Zero-shot 3D Segmentation of Abdominal Organs in CT Scans Using Segment Anything Model 2: Adapting Video Tracking Capabilities for 3D Medical Imaging

Purpose: This study aimed to evaluate the zero-shot performance of Segment Anything Model 2 (SAM 2) in 3D segmentation of abdominal organs in CT scans, leveraging its video tracking capabilities for volumetric medical imaging. Materials and Methods: Using a subset of the TotalSegmentator CT dataset (n=123) from 8 different institutions, we assessed SAM 2's ability to segment 8 abdominal organs. Segmentation was initiated from three different Z-coordinate levels (caudal, mid, and cranial levels) of each organ. Performance was measured using the Dice similarity coefficient (DSC). We also analyzed organ volumes to contextualize the results. Results: As a zero-shot approach, larger organs with clear boundaries demonstrated high segmentation performance, with mean(median) DSCs as follows: liver 0.821(0.898), left kidney 0.870(0.921), right kidney 0.862(0.935), and spleen 0.891(0.932). Smaller or less defined structures showed lower performance: gallbladder 0.531(0.590), pancreas 0.361(0.359), and adrenal glands 0.203-0.308(0.109-0.231). Significant differences in DSC were observed depending on the starting initial slice of segmentation for different organs. A moderate positive correlation was observed between volume size and DSCs (Spearman's rs = 0.731, P <.001 at caudal-level). DSCs exhibited high variability within organs, ranging from near 0 to almost 1.0, indicating substantial inconsistency in segmentation performance between scans. Conclusion: SAM 2 demonstrated promising zero-shot performance in segmenting certain abdominal organs in CT scans, particularly larger organs with clear boundaries. The model's ability to segment previously unseen targets without additional training highlights its potential for cross-domain generalization in medical imaging. However, improvements are needed for smaller and less defined structures.

Synthetic data generation method for hybrid image-tabular data using two generative adversarial networks

The generation of synthetic medical records using generative adversarial networks (GANs) has become increasingly important for addressing privacy concerns and promoting data sharing in the medical field. In this paper, we propose a novel method for generating synthetic hybrid medical records consisting of chest X-ray images (CXRs) and structured tabular data (including anthropometric data and laboratory tests) using an auto-encoding GAN ({\alpha}GAN) and a conditional tabular GAN (CTGAN). Our approach involves training a {\alpha}GAN model on a large public database (pDB) to reduce the dimensionality of CXRs. We then applied the trained encoder of the GAN model to the images in original database (oDB) to obtain the latent vectors. These latent vectors were combined with tabular data in oDB, and these joint data were used to train the CTGAN model. We successfully generated diverse synthetic records of hybrid CXR and tabular data, maintaining correspondence between them. We evaluated this synthetic database (sDB) through visual assessment, distribution of interrecord distances, and classification tasks. Our evaluation results showed that the sDB captured the features of the oDB while maintaining the correspondence between the images and tabular data. Although our approach relies on the availability of a large-scale pDB containing a substantial number of images with the same modality and imaging region as those in the oDB, this method has the potential for the public release of synthetic datasets without compromising the secondary use of data.

X2CT-FLOW: Reconstruction of multiple volumetric chest computed tomography images with different likelihoods from a uni- or biplanar chest X-ray image using a flow-based generative model

We propose X2CT-FLOW for the reconstruction of volumetric chest computed tomography (CT) images from uni- or biplanar digitally reconstructed radiographs (DRRs) or chest X-ray (CXR) images on the basis of a flow-based deep generative (FDG) model. With the adoption of X2CT-FLOW, all the reconstructed volumetric chest CT images satisfy the condition that each of those projected onto each plane coincides with each input DRR or CXR image. Moreover, X2CT-FLOW can reconstruct multiple volumetric chest CT images with different likelihoods. The volumetric chest CT images reconstructed from biplanar DRRs showed good agreement with ground truth images in terms of the structural similarity index (0.931 on average). Moreover, we show that X2CT-FLOW can actually reconstruct such multiple volumetric chest CT images from DRRs. Finally, we demonstrate that X2CT-FLOW can reconstruct multiple volumetric chest CT images from a real uniplanar CXR image.