リサーチアシスタントの韓さんとRCMBの村尾特任准教授、佐藤センター長らによる、脳MRI画像から早期のアルツハイマー病を検出する研究の発表がCIBB 2019に採択されました。

GANを利用した教師なし学習によって健常者の脳MRI画像を生成し、その生成画像との逸脱度を測ることでテスト画像に潜む異常を検出するアルゴリズムを提案しました。今回はアルツハイマー病患者の脳MRI画像をテストに用いましたが、このアルゴリズムは教師なし学習をベースにしているので、脳の器質的変化をきたす他の疾患の検出にも利用可能です。

Changhee Han, Leonardo Rundo, Kohei Murao, Zoltán Ádám Milacski, Kazuki Umemoto, Hideki Nakayama and Shin'ichi Satoh

GAN-based Multiple Adjacent Brain MRI Slice Reconstruction for Unsupervised Alzheimer's Disease Diagnosis

16th International Conference on Computational Intelligence methods for Bioinformatics and Biostatistics (CIBB), 4-6 September 2019, Bergamo, Italy

Abstract

Unsupervised learning can discover various unseen diseases, relying on large-scale unannotated medical images of healthy subjects. Towards this, unsupervised methods reconstruct a single medical image to detect outliers either in the learned feature space or from high reconstruction loss. However, without considering continuity between multiple adjacent images, they cannot directly discriminate diseases composed of the accumulation of subtle anatomical anomalies, such as Alzheimer's Disease (AD). Moreover, no study shows how unsupervised anomaly detection is associated with disease stages. Therefore, we propose a two-step method using Generative Adversarial Network-based multiple adjacent brain MRI slice reconstruction to detect AD at various stages: (Reconstruction) Wasserstein loss with Gradient Penalty + L1 loss---trained on 3 healthy slices to reconstruct the next 3 ones---reconstructs unseen healthy/AD cases; (Diagnosis) Average/Maximum loss (e.g., L2 loss) per scan discriminates them, comparing the reconstructed/ground truth images. The results show that we can reliably detect AD at a very early stage with Area Under the Curve (AUC) 0.780 while also detecting AD at a late stage much more accurately with AUC 0.917; since our method is unsupervised, it should also discover and alert any anomalies including rare disease.

https://arxiv.org/abs/1906.06114

CIBB (Computational Intelligence methods for Bioinformatics and Biostatistics) は計算機知能をバイオインフォマティクスや医療統計などへ適用する手法を議論する国際学会の一つです。今年の年会はイタリアのベルガモで9月4日から開催されます。