Introduction:

Although automatic segmentation has been increasingly applied to hyper-acute to acute ischemic stroke using DWI, the performance of segmentation varies widely from study to study with the Dice similarity coefficient (DSC) ranging from 0.34 [1] to 0.85 [2]. The wide variation might be attributed to factors including the different images (DWI or ADC maps) serving for ground truth segmentation [3,4], different segmentation methods (manual segmentation or thresholding segmentation) defining the ground truth, and different sizes of infarction. We hypothesized that performance of automatic segmentation is dependent on the ADC threshold defining ground truth and the size of the ischemic infarction. The aim of this study was to examine the role of ADC threshold and lesion size on segmentation performance of a deep learning method, U-net [5], for hyper-acute to acute ischemic infarction.

Materials and Methods:

This study initially enrolled 363 patients in the China Medical University Hospital (CMUH) and 120 patients in the China Medical University Hsinchu Hospital (CMUHCH) with clinical symptoms of acute ischemic infarction no more than 7 days of last known onset. MRI scans were performed using a 1.5-T scanner (Signa HDxt, GE) and a 3.0-T scanner (Signa HDx, GE). Diffusion-weighted images were acquired on axial planes with diffusion gradients (b factors) of 0 and 1000 sec/mm2 applied in each of three orthogonal directions using single shot spin-echo echoplanar acquisitions. The scan parameters included a field of view of 240 × 240 mm, a matrix of 128 × 128, a bandwidth of 651 Hz/pixel, and a section thickness of 5 mm. ADC maps were generated via pixel-by-pixel computation from b₀ and b₁₀₀₀ images using a mono-exponential model using a formula of SI_b1000=SI_b0 × e^-bD. Segmentation of ischemic infarction by manually contouring the hyperintense lesion on DWI was independently performed by two neuroradiologists. To evaluate the effect of ADC threshold on the performance of segmentation, an ADC threshold was added as a variable in determining the ground truth. The ADC threshold ranged from 0.6 × 10^-3 mm²/sec to 1.0 × 10^-3 mm²/sec at an interval of 0.1 × 10^-3 mm²/sec. In the beginning, 350 original DWI images and corresponding ADC maps with stroke in CHUH were used to train artificial intelligent (AI) model for stroke lesion detection. Another 350 augmented DWI and ADC maps were generated by flipping left and right in original images. Accordingly, 700 DWI and ADC with stroke and 300 normal DWI and ADC were used for AI model training. Data were split into 80% of for training and 20% for validation. U-nets with 4 layers and 64 features were built, and 8 images as batch-size were randomly selected for each train. There were 100 interactions in each epoch, and total 150 epochs were used. Finally, a total of 6 U-net models were trained based on 6 stroke labels (original and with ADC mask 0.6 ~ 1.0 ×10^-3 mm²/sec) for each observer. Fifty cases from CMUH were used for testing and 30 cases from CMUHCH were usef for externally validating the AI models for stroke lesion detection. A prediction threshold (0.8) was applied to make prediction of stroke. Dice coefficients between prediction and ground truth were calculated in 6 different types of GT to present the effect of size dependent GT by different ADC masks. ANOVA and Bouferroni post hoc analyses were applied for statistical analysis.

Result:

Figure 1 demonstrates the original b₀, b₁₀₀₀ images and ADC map for a patient with acute ischemic stroke (a) and shows the GTs (upper column), AI predictions (middle column) and overlayed (lower column) images of the stroke lesion under different ADC thresholds ranging from 0.6 ~ 1.0 ×10^-3 mm²/sec. Figure 2 demonstrates the DSCs between the GT and the prediction in different ADC thresholds and different stroke sizes. It shows a trend of higher DSC in larger stroke lesion and lower ADC threshold.

Discussion:

Our results showed significant difference across different methods in defining the ground truth and different sizes of stroke lesions. The hyperintensity on DWI might be attributed not only to the cytotoxic edema of stroke lesion [6] but also the vasogenic edema [7]. In addition, cerebral sulci included in the manually segmented GT on DWI were gradually removed while a lower ADC threshold was applied. Our results supports the hypothesis that performance of automatic segmentation is dependent on the ADC threshold defining ground truth and the size of the ischemic infarction.

Acknowledgements

No acknowledgement found.