A deep-learning approach for direct whole-heart mesh reconstruction

Automated construction of surface geometries of cardiac structures from volumetric medical images is important for a number of clinical applications. While deep-learning-based approaches have demonstrated promising reconstruction precision, these approaches have mostly focused on voxel-wise segmentation followed by surface reconstruction and post-processing techniques. However, such approaches suffer from a number of limitations including disconnected regions or incorrect surface topology due to erroneous segmentation and stair-case artifacts due to limited segmentation resolution. We propose a novel deep-learning-based approach that directly predicts whole heart surface meshes from volumetric CT and MR image data. Our approach leverages a graph convolutional neural network to predict deformation on mesh vertices from a pre-defined mesh template to reconstruct multiple anatomical structures in a 3D image volume. Our method demonstrated promising performance of generating whole heart reconstructions with as good or better accuracy than prior deep-learning-based methods on both CT and MR data. Furthermore, by deforming a template mesh, our method can generate whole heart geometries with better anatomical consistency and produce high-resolution geometries from lower resolution input image data. Our method was also able to produce temporally-consistent surface mesh predictions for heart motion from CT or MR cine sequences, and therefore can potentially be applied for efficiently constructing 4D whole heart dynamics. Our code and pre-trained networks are available at https://github.com/fkong7/MeshDeformNet.

Computer-Assisted Aneurysm Growth Evaluation and Detection (AGED): Comparison with Clinical Aneurysm Follow-Up

Background and Purpose

Since growing intracranial aneurysms (IA) are more likely to rupture, detecting growth is an important part of unruptured IA follow-up. Recent studies have consistently shown that detecting IA growth can be challenging, especially in smaller aneurysms. In this study, we present an automated computational method to assist detecting aneurysm growth.

Materials and Methods

An analysis program, Aneurysm Growth Evaluation & Detection (AGED) based on IA images was developed. To verify the program can satisfactorily detect clinical aneurysm growth, we performed this comparative study using clinical determinations of growth during IA follow-up as a gold standard. Patients with unruptured, saccular IA followed by diagnostic brain CTA to monitor IA progression were reviewed. 48 IA image series from twenty longitudinally-followed ICA IA were analyzed using AGED. A set of IA morphologic features were calculated. Nonparametric statistical tests and ROC analysis were performed to evaluate the performance of each feature for growth detection.

Results

The set of automatically calculated morphologic features demonstrated comparable results to standard, manual clinical IA growth evaluation. Specifically, automatically calculated HMAX was superior (AUC = 0.958) at distinguishing growing and stable IA, followed by V, and SA (AUC = 0.927 and 0.917, respectively).

Conclusion

Our findings support automatic methods of detecting IA growth from sequential imaging studies as a useful adjunct to standard clinical assessment. AGED-generated growth detection shows promise for characterization and detection of IA growth and time-saving comparing with manual measurements.