| Autor: |
Ke Fang, Bokai Yang, Xingyu Li |
| Rok vydání: |
2021 |
| Předmět: |
|
| Zdroj: |
Towards the Automatization of Cranial Implant Design in Cranioplasty II ISBN: 9783030926519 |
| DOI: |
10.1007/978-3-030-92652-6_8 |
| Popis: |
The development of automatic skull reconstruction methods has dramatically reduced the time and expense to repair skull defects. In this study, an ensemble-learning-based method is proposed for skull implant prediction. To overcome the potential overfit problem in 3-D volume analysis using deep learning, a set of 2-D defective skull images is generated by slicing 3-D volumes along the X, Y, and Z axes. We further introduce an RNN model in this method to compensate for the loss of global skull information in the 2-D implant prediction. Over the implant estimation problem in Task 1 of the AutoImplant 2021 challenge, we observe a considerable performance boost from our averaging ensemble strategy and noise removal filtering. The codes for our method as well as our pretrained models is accessible with https://github.com/YouJianFengXue/Cranial-implant-prediction-by-learning-an-ensemble-of-slice-based-skull-completion-networks. |
| Databáze: |
OpenAIRE |
| Externí odkaz: |
|
