A deep semantic segmentation correction network for multi-model tiny lesion areas detection

BMC Medical Informatics and Decision Making

Yue Liu¹  Benzheng Wei¹  Tianyang Li¹  Xiang Li²  Bin Li³  Jie Gan³  Zhensong Wang³ 

[1] Center for Medical Artificial Intelligence, Shandong University of Traditional Chinese Medicine, 266112, Qingdao, China;Qingdao Academy of Chinese Medical Sciences, Shandong University of Traditional Chinese Medicine, 266112, Qingdao, China;Center for Medical Artificial Intelligence, Shandong University of Traditional Chinese Medicine, 266112, Qingdao, China;Qingdao Academy of Chinese Medical Sciences, Shandong University of Traditional Chinese Medicine, 266112, Qingdao, China;College of Intelligence and Information Engineering, Shandong University of Traditional Chinese Medicine, 250355, Jinan, China;Radiology Department, Second Affiliated Hospital of Shandong University of Traditional Chinese Medicine, 250001, Jinan, China;

关键词: White matter hyperintensities;    Focal cerebral ischemia;    Lacunar infarct;    Magnetic resonance imaging;    Multi-modality;   

DOI  :  10.1186/s12911-021-01430-z

来源: Springer

【 摘 要 】

BackgroundSemantic segmentation of white matter hyperintensities related to focal cerebral ischemia (FCI) and lacunar infarction (LACI) is of significant importance for the automatic screening of tiny cerebral lesions and early prevention of LACI. However, existing studies on brain magnetic resonance imaging lesion segmentation focus on large lesions with obvious features, such as glioma and acute cerebral infarction. Owing to the multi-model tiny lesion areas of FCI and LACI, reliable and precise segmentation and/or detection of these lesion areas is still a significant challenge task.MethodsWe propose a novel segmentation correction algorithm for estimating the lesion areas via segmentation and correction processes, in which we design two sub-models simultaneously: a segmentation network and a correction network. The segmentation network was first used to extract and segment diseased areas on T2 fluid-attenuated inversion recovery (FLAIR) images. Consequently, the correction network was used to classify these areas at the corresponding locations on T1 FLAIR images to distinguish between FCI and LACI. Finally, the results of the correction network were used to correct the segmentation results and achieve segmentation and recognition of the lesion areas.ResultsIn our experiment on magnetic resonance images of 113 clinical patients, our method achieved a precision of 91.76% for detection and 92.89% for classification, indicating a powerful method to distinguish between small lesions, such as FCI and LACI.ConclusionsOverall, we developed a complete method for segmentation and detection of WMHs related to FCI and LACI. The experimental results show that it has potential clinical application potential. In the future, we will collect more clinical data and test more types of tiny lesions at the same time.

【 授权许可】

CC BY