Automatic segmentation of cerebral infarcts in follow-up computed tomography images with convolutional neural networks

Abstract

Background and purpose: Infarct volume is a valuable outcome measure in treatment trials of acute ischemic stroke and is strongly associated with functional outcome. Its manual volumetric assessment is, however, too demanding to be implemented in clinical practice.

Objective: To assess the value of convolutional neural networks (CNNs) in the automatic segmentation of infarct volume in follow-up CT images in a large population of patients with acute ischemic stroke.

Materials and methods: We included CT images of 1026 patients from a large pooling of patients with acute ischemic stroke. A reference standard for the infarct segmentation was generated by manual delineation. We introduce three CNN models for the segmentation of subtle, intermediate, and severe hypodense lesions. The fully automated infarct segmentation was defined as the combination of the results of these three CNNs. The results of the three-CNNs approach were compared with the results from a single CNN approach and with the reference standard segmentations.

Results: The median infarct volume was 48 mL (IQR 15-125 mL). Comparison between the volumes of the three-CNNs approach and manually delineated infarct volumes showed excellent agreement, with an intraclass correlation coefficient (ICC) of 0.88. Even better agreement was found for severe and intermediate hypodense infarcts, with ICCs of 0.98 and 0.93, respectively. Although the number of patients used for training in the single CNN approach was much larger, the accuracy of the three-CNNs approach strongly outperformed the single CNN approach, which had an ICC of 0.34.

Conclusion: Convolutional neural networks are valuable and accurate in the quantitative assessment of infarct volumes, for both subtle and severe hypodense infarcts in follow-up CT images. Our proposed three-CNNs approach strongly outperforms a more straightforward single CNN approach.

Keywords: CT; stroke; technique; thrombectomy.

Figure 1

Histogram of average infarct intensities of the manually delineated infarcts. The left CT image at the top displays a relatively old infarct with a severe hypodensity; in the middle, an intermediate old infarct is shown; and the image on the right shows a relatively young infarct with a subtle hypodensity.

Figure 2

Top: Comparison of the infarct volume of the results from the three-CNNs approach (y axis) with the reference to infarct volume (x axis). Bottom: Bland-Altman plots of the infarct volumes. The difference in the volume determination is given along the y axis, and the average of the automated and reference infarct volume is depicted along the x axis. The different columns show separate severe, intermediate, and subtle hypodensity infarcts.

Figure 3

Sample results. from left to right we have input image, union of the segmentation results, and reference segmentation. For simplicity, in the center column we rendered the hemorrhages (blue) over the subtle infarcts (yellow), subtle infarcts over standard infarcts (orange), and standard infarcts over severe infarcts (red). The Dice coefficients from top to bottom were 0.10, 0.26, 0.40, 0.55, and 0.70. In the left colum the original images are shown. The right shows the merged segmentations.