Deep Learning for Automated Measurement of Hemorrhage and Perihematomal Edema in Supratentorial Intracerebral Hemorrhage

Abstract

Background and Purpose— Volumes of hemorrhage and perihematomal edema (PHE) are well-established biomarkers of primary and secondary injury, respectively, in spontaneous intracerebral hemorrhage. An automated imaging pipeline capable of accurately and rapidly quantifying these biomarkers would facilitate large cohort studies evaluating underlying mechanisms of injury. Methods— Regions of hemorrhage and PHE were manually delineated on computed tomography scans of patients enrolled in 2 intracerebral hemorrhage studies. Manual ground-truth masks from the first cohort were used to train a fully convolutional neural network to segment images into hemorrhage and PHE. The primary outcome was automated-versus-human concordance in hemorrhage and PHE volumes. The secondary outcome was voxel-by-voxel overlap of segmentations, quantified by the Dice similarity coefficient (DSC). Algorithm performance was validated on 84 scans from the second study. Results— Two hundred twenty-four scans from 124 patients with supratentorial intracerebral hemorrhage were used for algorithm derivation. Median volumes were 18 mL (interquartile range, 8–43) for hemorrhage and 12 mL (interquartile range, 5–30) for PHE. Concordance was excellent (0.96) for automated quantification of hemorrhage and good (0.81) for PHE, with DSC of 0.90 (interquartile range, 0.85–0.93) and 0.54 (0.39–0.65), respectively. External validation confirmed algorithm accuracy for hemorrhage (concordance 0.98, DSC 0.90) and PHE (concordance 0.90, DSC 0.55). This was comparable with the consistency observed between 2 human raters (DSC 0.90 for hemorrhage, 0.57 for PHE). Conclusions— We have developed a deep learning-based imaging algorithm capable of accurately measuring hemorrhage and PHE volumes. Rapid and consistent automated biomarker quantification may accelerate powerful and precise studies of disease biology in large cohorts of intracerebral hemorrhage patients.

Efficiency of a deep learning-based artificial intelligence diagnostic system in spontaneous intracerebral hemorrhage volume measurement

“…Several different deep learning models have been developed for ICH automated quantification. Research has supported that the volume of ICH segmented by deep learning models, which is faster than manual CTP, yields similar results to CTP; [ 18 , 19 , 28 – 30 ]this is consistent with our present results.…”

“…Accordingly, its network can learn by analyzing the training data and make predictions as new data are entered, requiring little manual engineering [ 15 , 17 ]. Some deep learning-based AI diagnosis systems have been developed to detect and segment cerebral hemorrhage,[ 18 , 19 ] but the accuracy of these algorithms and their practical clinical value require further external verification. IVH has been associated with mortality rates as high as 50–75% [ 20 , 21 ] and increasing the accuracy of the definitions of IVH volume could improve its ability to predict outcomes.…”

“…IVH has been associated with mortality rates as high as 50–75% [ 20 , 21 ] and increasing the accuracy of the definitions of IVH volume could improve its ability to predict outcomes. [ 22 , 23 ] However, the presence of IVH may blur the boundaries between intraparenchymal and intraventricular blood, and consequently, it’s challenging to accurately differentiate IVH from the adjacent parenchymal hematoma, even for experienced human raters [ 18 , 24 , 25 ]. Researchers have tried different techniques to find a more efficient way to define hematoma expansion including IVH expansion.…”

Perihematomal Edema After Intracerebral Hemorrhage: An Update on Pathogenesis, Risk Factors, and Therapeutic Advances

“…However, the best dice value was only 0.71. These findings indicate that automatic PHE segmentation is considerably more difficult than hematoma segmentation because of the lower clarity of PHE on CT scans ( 58 , 59 ), necessitating refinement of the performance of automatic PHE segmentation.…”

This was the year that was: brain barriers and brain fluid research in 2019

“…For example, in brain, machine learning can be used for image analysis [17,18]. Machine learning is poised to impact brain barriers and brain fluids research in, for example, analysis of brain edema on radiographic images [19,20] or potentially to predict brain drug penetration [21,22].…”