High-resolution Cranial Implant Prediction via Patch-wise Training

Autor:	Jan Egger, Yuan Jin, Jianning Li
Jazyk:	angličtina
Rok vydání:	2020
Předmět:	Similarity (geometry) Computer science business.industry Deep learning Skull Task (computing) medicine.anatomical_structure Hausdorff distance Test set medicine Computer vision Implant Artificial intelligence business Cranial implant
Zdroj:	Towards the Automatization of Cranial Implant Design in Cranioplasty ISBN: 9783030643263 AutoImplant@MICCAI
DOI:	10.13140/rg.2.2.25455.25765
Popis:	In this study, we proposed two methods for AutoImplant (https://autoimplant.grand-challenge.org/) - the cranial implant design challenge. The shape of the implant is predicted based on the inputted defective skull. This task can be accomplished either by directly predicting the implant with the defective skull, or indirectly rebuilding the complete skull and then taking the difference between the defective and complete skulls. In our work, a deep learning model is applied to automatically predict the implant. In order to solve the problem that high resolution images can often not be directly inputted to the deep learning model, two proposed methods of resize and patch-based are examined. On the test set, the proposed resize method achieves an average dice similarity score (DSC) of 0.7350 and a Hausdorff distance (HD) of 7.2425 mm, while the proposed patch-based method achieves an average DSC of 0.8887 and a HD of 5.5339 mm.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_dedup___::ea4e4cf15e6cd29065dbaeb08623cac9 Zobrazit plný text záznamu