MedMeshCNN – Enabling MeshCNN for Medical Surface Models

09/10/2020
by   Lisa Schneider, et al.
28

Background and objective: MeshCNN is a recently proposed Deep Learning framework that drew attention due to its direct operation on irregular, non-uniform 3D meshes. On selected benchmarking datasets, it outperformed state-of-the-art methods within classification and segmentation tasks. Especially, the medical domain provides a large amount of complex 3D surface models that may benefit from processing with MeshCNN. However, several limitations prevent outstanding performances of MeshCNN on highly diverse medical surface models. Within this work, we propose MedMeshCNN as an expansion for complex, diverse, and fine-grained medical data. Methods: MedMeshCNN follows the functionality of MeshCNN with a significantly increased memory efficiency that allows retaining patient-specific properties during the segmentation process. Furthermore, it enables the segmentation of pathological structures that often come with highly imbalanced class distributions. Results: We tested the performance of MedMeshCNN on a complex part segmentation task of intracranial aneurysms and their surrounding vessel structures and reached a mean Intersection over Union of 63.24%. The pathological aneurysm is segmented with an Intersection over Union of 71.4%. Conclusions: These results demonstrate that MedMeshCNN enables the application of MeshCNN on complex, fine-grained medical surface meshes. The imbalanced class distribution deriving from the pathological finding is considered by MedMeshCNN and patient-specific properties are mostly retained during the segmentation process.

READ FULL TEXT

page 4

page 5

page 6

research
03/21/2021

Geo-Spatiotemporal Features and Shape-Based Prior Knowledge for Fine-grained Imbalanced Data Classification

Fine-grained classification aims at distinguishing between items with si...
research
11/20/2021

Medical Knowledge-Guided Deep Learning for Imbalanced Medical Image Classification

Deep learning models have gained remarkable performance on a variety of ...
research
06/28/2021

Progressive Class-based Expansion Learning For Image Classification

In this paper, we propose a novel image process scheme called class-base...
research
01/18/2023

Curvilinear object segmentation in medical images based on ODoS filter and deep learning network

Automatic segmentation of curvilinear objects in medical images plays an...
research
06/11/2019

3-D Surface Segmentation Meets Conditional Random Fields

Automated surface segmentation is important and challenging in many medi...
research
01/26/2021

Boosting Segmentation Performance across datasets using histogram specification with application to pelvic bone segmentation

Accurate segmentation of the pelvic CTs is crucial for the clinical diag...

Please sign up or login with your details

Forgot password? Click here to reset