| 研究課題/領域番号 |
20K23356
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| 研究機関 | 国立研究開発法人理化学研究所 |
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研究代表者 |
Rachmadi Muhammad 国立研究開発法人理化学研究所, 脳神経科学研究センター, 研究員 (60874881)
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| 研究期間 (年度) |
2020-09-11 – 2022-03-31
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| キーワード | White matter lesions / Progression of WMLs / Disease prediction model / Deep learning |
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| 研究実績の概要 |
In the last fiscal year, we focused on developing supervised deep learning model for predicting the progression of white matter hyperintensities (WMHs). Based on our previous study, the biggest challenge is to correctly segment growing and shrinking WMHs. We investigated several different modifications of U-Net to improve our last study on the same topic, and we have successfully produced some good results by using multi-head segmentation layers and generative adversarial training. Our latest result shows that our current approach successfully improved segmentation of growing and shrinking WMHs up to 0.3 Dice similarity coefficient (DSC) from 0.1 DSC in our previous study.
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| 現在までの達成度 (区分) |
現在までの達成度 (区分)
2: おおむね順調に進展している
理由
We have successfully produced good results that we can publish in an international conference/workshop.
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| 今後の研究の推進方策 |
Firstly, we plan to write and publish the latest results in an international conference/workshop this summer (June/July 2021). Secondly, we plan to develop a new model based on Bayesian network and combine it with our latest model to estimate uncertainties in the model. While we have successfully produced better segmentation for growing and shrinking WMHs (from 0.1 DSC in our previous study to 0.3 DSC in this study), it is still not ideal for clinical usage. We believe that a model that can estimate uncertainties has a higher chance to be used in clinical setting. We plan to write and publish a journal paper for the completion of this project.
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| 次年度使用額が生じた理由 |
The funding will be used to participate in international conference/workshop related to the project (i.e., medical image computation and machine learning).
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