Evaluation of Diffusion Lesion Volume Measurements in Acute Ischemic Stroke Using Encoder-Decoder Convolutional Network

Abstract

Background and Purpose— Automatic segmentation of cerebral infarction on diffusion-weighted imaging (DWI) is typically performed based on a fixed apparent diffusion coefficient (ADC) threshold. Fixed ADC threshold methods may not be accurate because ADC values vary over time after stroke onset. Deep learning has the potential to improve the accuracy, provided that a large set of correctly annotated lesion data is used for training. The purpose of this study was to evaluate deep learning–based methods and compare them with commercial software in terms of lesion volume measurements. Methods— U-net, an encoder-decoder convolutional neural network, was adopted to train segmentation models. Two U-net models were developed: a U-net (DWI+ADC) model, trained on DWI and ADC data, and a U-net (DWI) model, trained on DWI data only. A total of 296 subjects were used for training and 134 for external validation. An expert neurologist manually delineated the stroke lesions on DWI images, which were used as the ground-truth reference. Lesion volume measurements from the U-net methods were compared against the expert’s manual segmentation and Rapid Processing of Perfusion and Diffusion (RAPID; iSchemaView Inc) analysis. Results— In external validation, U-net (DWI+ADC) showed the highest intraclass correlation coefficient with manual segmentation (intraclass correlation coefficient, 1.0; 95% CI, 0.99–1.00) and sufficiently high correlation with the RAPID results (intraclass correlation coefficient, 0.99; 95% CI, 0.98–0.99). U-net (DWI+ADC) and manual segmentation resulted in the smallest 95% Bland-Altman limits of agreement (−5.31 to 4.93 mL) with a mean difference of −0.19 mL. Conclusions— The presented deep learning–based method is fully automatic and shows a high correlation of diffusion lesion volume measurements with manual segmentation and commercial software. The method has the potential to be used in patient selection for endovascular reperfusion therapy in the late time window of acute stroke.

Automatic Assessment of ASPECTS Using Diffusion-Weighted Imaging in Acute Ischemic Stroke Using Recurrent Residual Convolutional Neural Network

“…Deep learning algorithms have been applied to medical imaging [ 9 ] and have led to an exciting opportunity for data-driven stroke management and guiding the diagnosis of acute ischemic stroke [ 30 , 31 ]. Recently, several studies have used CNN algorithms for application in acute ischemic lesions [ 32 , 33 , 34 , 35 , 36 ] and provided effective tools for automatic lesion segmentation or volume calculation. Other studies have focused on developing a deep learning-based approach for detection or identification of large vessel occlusion from CT angiography [ 37 , 38 ].…”

Novel Estimation of Penumbra Zone Based on Infarct Growth Using Machine Learning Techniques in Acute Ischemic Stroke

“…Second, a custom software tool was used to measure the pre-treatment PWI and DWI lesion volumes based on Tmax and ADC thresholds, for the target mismatch assessment. ML-based methods for lesion volume estimation may be alternative tools for mismatch evaluation [29,30]. Third, only ADC and rTTP were used for the ML model development in the present study.…”

Suitability of Deep Weakly Supervised Learning to detect Acute Ischemic Stroke and Hemorrhagic Infarction Lesions Using Diffusion-weighted Imaging

“…The accurate annotation of AIS lesions from a large number of images requires tremendous time. [21] In this paper, we proposed a weakly supervised method to detect lesions, which was very effective for detecting AIS lesions. ResNet had more advantages than VGG.…”