Deep learning algorithms for automatic segmentation of acute cerebral infarcts on diffusion-weighted images: Effects of training data sample size, transfer learning, and data features

Abstract

Background: Deep learning-based artificial intelligence techniques have been developed for automatic segmentation of diffusion-weighted magnetic resonance imaging (DWI) lesions, but currently mostly using single-site training data with modest sample sizes. Objective: To explore the effects of 1) various sample sizes of multi-site vs. single-site training data, 2) domain adaptation, the utilization of target domain data to overcome the domain shift problem, where a model that performs well in the source domain proceeds to perform poorly in the target domain, and 3) data sources and features on the performance and generalizability of deep learning algorithms for the segmentation of infarct on DW images. Methods: In this nationwide multicenter study, 10,820 DWI datasets from 10 hospitals (Internal dataset) were used for the training-and-validation (Training-and-validation dataset with six progressively larger subsamples: n=217, 433, 866, 1,732, 4,330, and 8,661 sets, yielding six algorithms) and internal test (Internal test dataset: 2,159 sets without overlapping sample) of 3D U-net algorithms for automatic DWI lesion segmentation. In addition, 476 DW images from one of the 10 hospitals (Single-site dataset) were used for training-and-validation (n=382) and internal test (n=94) of another algorithm. Then, 2,777 DW images from a different hospital (External dataset) and two ancillary test datasets (I, n=50 from three different hospitals; II, n=250 from Ischemic Stroke Lesion Segmentation Challenge 2022) were used for external validation of the seven algorithms, testing each algorithm performance vs. manual segmentation gold standard using DICE scores as a figure of merit. Additional tests of the six algorithms were performed after stratification by infarct volume, infarct location, and stroke onset-to-imaging time. Domain Adaptation was performed to fine-tune the algorithms with subsamples (50, 100, 200, 500, and 1000) of the 2,777 External dataset, and its effect was tested using a) 1,777 DW images (from the External dataset, without overlapping sample) and b) 2,159 DW images from the Internal test dataset. Results: Mean age of the 8,661 patients in the Training-and-validation dataset was 67.9 years (standard deviation 12.9), and 58.9% (n = 4,431) were male. As the subsample size of the multi-site dataset was increased from 217 to 1,732, algorithm performance increased sharply, with DSC scores rising from 0.58 to 0.65. When the sample size was further increased to 4,330 and 8,661, DSC increased only slightly (to 0.68 and 0.70, respectively). Similar results were seen in external tests. Although a deep learning algorithm that was developed using the Single-site dataset achieved DSC of 0.70 (standard deviation 0.23) in internal test, it showed substantially lower performance in the three external tests, with DSC values of 0.50, 0.51, and 0.33, respectively (all p < 0.001). Stratification of the Internal test dataset and the External dataset into small (< 1.7 ml; n = 994 and 1,046, respectively), medium (1.7-14.0 ml; n = 587 and 904, respectively), and large (> 14.0; n = 446 and 825, respectively) infarct size groups, showed the best performance (DSCs up to ~0.8) in the large infarct group, lower (up to ~0.7) in the medium infarct group, and the lowest (up to ~0.6) in the small infarct group. Deep learning algorithms performed relatively poorly on brainstem infarcts or hyperacute (< 3h) infarcts. Domain adaptation, the use of a small subsample of external data to re-train the algorithm, was successful at improving algorithm performance. The algorithm trained with the 217 DW images from the Internal dataset and fine-tuned with an additional 50 DW images from the External dataset, had equivalent performance to the algorithm trained using a four-fold higher number (n=866) of DW images using the Internal dataset only (without domain adaptation). Conclusion: This study using the largest DWI data to date demonstrates that: a) multi-site data with ~1,000 DW images are required for developing a reliable infarct segmentation algorithm, b) domain adaptation could contribute to generalizability of the algorithm, and c) further investigation is required to improve the performance for segmentation of small or brainstem infarcts or hyperacute infarcts.

Deep Learning-Based Automatic Classification of Ischemic Stroke Subtype Using Diffusion-Weighted Images

“…Brain DWIs were preprocessed by (1) skull stripping using the Gaussian blur and Otsu’s threshold [ 18 ], (2) applying N4 bias field correction using the SimpleITK library, and (3) performing image signal normalization. After the preprocessing, infarct areas on DWI were automatically segmented using the aforementioned validated 3D U-net algorithm (JLK-DWI, JLK Inc., Seoul, Korea) [ 16 , 17 ]. The segmented infarct masks from raw DWIs were stacked and condensed into three 2D X, Y, Z-axis images to ensure consistent data input regardless of the number of slices ( Supplementary Figure 3 ).…”

Automated detection of large vessel occlusion: a multicenter study validating efficacy and proving clinical implications

“…Infarct volumes on DWI were calculated using a validated software package (JLK-DWI, JLK Inc., Seoul, Korea). 16,17 Segmented infarct area was meticulously supervised by an experienced vascular neurologist (W-S. Ryu).…”

Multicenter validation of artificial intelligence software predicting large vessel occlusion using noncontrast brain CT

“…Infarct volumes on DWI were calculated using a validated software package (JLK-DWI, JLK Inc., Seoul, Korea). [13][14][15] The segmentation of the infarct area was carefully overseen by an experienced vascular neurologist (J-W. C). In cases where automated segmentation was inaccurate, manual corrections were applied to ensure precise segmentation.…”