AUTHOR=Wang Jincheng , Chen Sijie , Liang Hui , Zhao Yilei , Xu Ziqi , Xiao Wenbo , Zhang Tingting , Ji Renjie , Chen Tao , Xiong Bing , Chen Feng , Yang Jun , Lou Haiyan 

TITLE=Fully Automatic Classification of Brain Atrophy on NCCT Images in Cerebral Small Vessel Disease: A Pilot Study Using Deep Learning Models

JOURNAL=Frontiers in Neurology

VOLUME=Volume 13 - 2022

YEAR=2022

URL=https://www.frontiersin.org/journals/neurology/articles/10.3389/fneur.2022.846348

DOI=10.3389/fneur.2022.846348

ISSN=1664-2295

ABSTRACT=Objective: Brain atrophy is an important imaging characteristic of cerebral small vascular disease (CSVD). Our study explores the linear measurement application on CT images of CSVD patients and develops a fully automatic brain atrophy classification model. The second aim was to compare it with the end-to-end Convolutional Neural Networks (CNNs) model.

Methods: 385 subjects including 107 no-atrophy brain, 185 mild atrophy and 93 severe atrophy were collected and randomly separated into training set (n=308) and test set (n=77). Key slices for linear measurement were manually identified and used to annotate 9 linear parameters and a binary classification of cerebral sulci widening. A linear-measurement based pipeline (2D model) was constructed for two-types (existence/nonexistence brain atrophy) or three-types classification (no/mild atrophy/severe atrophy). For comparison, an end-to-end CNN model (3D deep learning model) for brain atrophy classification was also developed. Furthermore, age and gender were integrated to the 2D and 3D models. The sensitivity, specificity, accuracy, average F1 score, receiver operating characteristics (ROC) for two-type classification and weighed kappa for three-type classification of the two models were compared. 

Results: Automated measurement of linear parameters and cerebral sulci widening achieved moderate to almost perfect agreement with manual annotation. In two-type atrophy classification, AUCs of the 2D model and 3D model were 0.953 and 0.941 with no significant difference (p = 0.250). The Weighted kappa of the 2D model and 3D model were 0.727 and 0.607 according to standard classification they displayed, mild atrophy and severe atrophy, respectively. Applying patient age and gender information improved classification performances of both 2D and 3D models in two-type and three-type classification of brain atrophy.

Conclusion: We provide a model composed of different modules that can classify CSVD-related brain atrophy on CT images automatically, using linear measurement. It has similar performance and better interpretability than the end-to-end CNNs model and may prove advantageous in the clinical setting.