Example of the Transport-Based Morphometry workflow.

( A ) NCCT scan registration and segmentation. A population-based high resolution NCCT template was used for NCCT registration prior to hematoma region segmentation. (B) Optimal transportation. Segmented hematoma regions, depicted as source images (I1, I2…) are transformed to the transport domain by pushing mass, represented as pixel intensity, from the source image to the reference image. This process computes optimal transportation maps, thereby representing the images as points on a high-dimensional Riemannian manifold. (C) Machine learning statistical analysis. In transport space, differences between given source images (I1, I2…) can be represented as a linear embedding of the difference between their computed transport maps. This permits effective application of statistical analysis methods to the high-dimensional data. (D) Discriminant image feature visualization. Representing data as points on a Riemannian manifold enables any point to be interrogated and inverted from the transport domain to the native image domain. This generates images of the discriminant features captured during statistical analysis. TBM is performed on volumetric NCCT images and two-dimensional slices are demonstrated for illustrative purposes. Abbreviations: PCA=principal components analysis, NCCT=non-contrast computed tomography, LDA=linear discriminant analysis.

Comparison of demographic and clinical information between the expansion and no expansion groups in the derivation and test datasets.

Abbreviations: INR = international normalized ratio, S.D. = standard deviation, n = number, IVH = intraventricular hemorrhage, NCCT = non-contrast computed tomography, IQR = interquartile range. * on admission.

Example of the preprocessing protocol.

(A) All NCCT scans were skull stripped and registered to a population-based high resolution NCCT template with dimensions of 256 x 256 x 256 and voxel spacings of 1 x 1 x 1mm. (B) example of a registered NCCT axial slice at presentation and (C) 24 hours in a patient with hematoma expansion. (F) the corresponding segmented and normalized hematoma image at presentation (G) and 24-hours for the same patient. (D) example of a registered NCCT axial slice at presentation (E) and 24 hours in a patient without hematoma expansion. (H) The corresponding normalized hematoma image at presentation (I) and 24-hours in the same patient. Examples of segmented presentation non-contrast computed tomography (NCCT) hematoma images separated into groups of (J) hematoma expansion (≥ 6mL hematoma volume increase at the 24 hour interval NCCT scan) and (K) no hematoma expansion (<6mL hematoma volume increase at the 24 hour interval NCCT scan), demonstrating a lack of visually discernible difference between the two groups. Abbreviations: NCCT = non-contrast computed tomography.

Results of the TBM model adjusted for location and clinical information in predicting 24-hour hematoma volume from the test dataset.

(A) Scatter plots showing the relationship between the hematoma image features in the test dataset projected onto the most correlated direction wcorr in transport space and change in hematoma volume from the presentation to the 24-hour NCCT scan. (B) Inverse transformations of three two-dimensional axial slice examples of the hematoma morphometric features found by the model to be associated with increasing growth, shown from left to right of the x-axis (C) Inverse transformations of the hematoma morphometric features overlaid onto the axial NCCT scan associated with least growth, left, and most growth, right. Abbreviations: NCCT = non-contrast computed tomography, TBM = transport-based morphometry, CC = correlation co-efficient σ = standard deviation of the pixel intensity distribution along wcorr.

Results of the TBM model adjusted for location and clinical information in predicting 24-hour hematoma expansion from the test dataset.

(A) Mean probability distributions of the hematoma image features in the test dataset projected onto the most discriminant direction w0 in transport space showing the degree of separation between the expansion (red) and no expansion (blue) groups by the learned pLDA classifier boundary. (B) AUROC analyses and corresponding 95% confidence intervals of the performance of the pLDA classifier in the test dataset for (C) Inverse transformations of three two-dimensional axial slice examples of the hematoma morphometric features found by the model to be associated with increasing likelihood of expansion, shown from left to right of the x-axis (D) Inverse transformations of the hematoma morphometric features overlaid onto the axial NCCT scan least associated with expansion, left, and most associated with expansion, right. Abbreviations: NCCT = non-contrast computed tomography, TBM = transport-based morphometry, AUROC = area under the receiver operator ocurve, pLDA = penalized linear discriminant analysis, σ = standard deviation of the pixel intensity distribution along w0.

Independent effects of hematoma location as a predictor of 24-hour hematoma expansion.

Two-dimensional examples of inverse transformations overlaid onto NCCT scans in the axial (top row), sagittal (second row) and coronal (third row) planes showing from left to right of the x-axis the hematoma morphometric features and location direction found by the TBM model to be associated with increasing likelihood of expansion. Abbreviations: NCCT = non-contrast computed tomography, TBM = transport-based morphometry, σ = standard deviation of the pixel intensity distribution along w0.

Comparisons of the performance of existing NCCT hematoma expansion prediction scores with comparison to the final TBM model adjusted for location and clinical information in test dataset.

Hematoma expansion was defined as ≥ 6mL hematoma volume increase from the presentation to the 24±6 hour NCCT scan. Abbreviations: AUROC = Area Under the Receiver Operator Curve, ROC = Receiver Operator Curve, TBM = transport-based morphometry, NCCT = non-contrast computed tomography, HEAVN = Heavn score, Brain = Brain score, HEP = Hematoma expansion prediction score, Pt = 10-point score, BAT = BAT score, TBM = transport-based morphometry.

Flow diagram illustrating the patient selection process.

Abbreviations: NCCT = Non-contrast computed tomography, DICOM = digital imaging and communications in medicine, ICH = intracerebral hemorrhage.

The intrinsic mean template image used for the linear optimal transportation framework for the (A) original hematoma image data and (B) location-adjusted translated hematoma image data.

Mean probability distributions of the hematoma image features in the internal validation cohort of the derivation dataset projected onto the most discriminant direction w0 in transport space showing the degree of separation between the expansion (red) and no expansion (blue) groups by the learned pLDA classifier boundary.

(A) TBM alone, (B) TBM adjusted for clinical information (C) TBM adjusted for location, (D) TBM adjusted for location and clinical information. Abbreviations: TBM = transport-based morphometry, pLDA = penalized linear discriminant analysis, σ = standard deviation of the pixel intensity distribution along w0.

AUROC analyses of the performance of the pLDA classifier in the internal validation cohort of the derivation dataset for (A) TBM alone, (B) TBM adjusted for clinical information (C) TBM adjusted for location, (D) TBM adjusted for location and clinical information.

Abbreviations: AUROC = Area Under the Receiver Operator Curve, ROC = Receiver Operator Curve, TBM = transport-based morphometry, pLDA = penalized linear discriminant analysis.

Mean probability distributions of the hematoma image features in the training cohort of the derivation dataset projected onto the most discriminant direction w0 in transport space showing the degree of separation between the expansion (red) and no expansion (blue) groups by the learned pLDA classifier boundary.

(A) TBM alone, (B) TBM adjusted for clinical information, (C) TBM adjusted for location, (D) TBM adjusted for location and clinical information. Abbreviations: TBM = transport-based morphometry, pLDA = penalized linear discriminant analysis, σ = standard deviation of the pixel intensity distribution along w0.

AUROC analyses of the performance of the pLDA classifier in the training cohort of the derivation dataset for (A) TBM alone, (B) TBM adjusted for clinical information, (C) TBM adjusted for location, (D) TBM adjusted for location and clinical information.

Abbreviations: AUROC = Area Under the Receiver Operator Curve, ROC = Receiver Operator Curve, TBM = transport-based morphometry, pLDA = penalized linear discriminant analysis.

Scatter plots showing the relationship between the hematoma image features in the internal validation cohort of the derivation dataset projected onto the most correlated direction wcorr in transport space and change in hematoma volume from the presentation to the 24-hour NCCT scan.

(A) TBM alone, (B) TBM adjusted for clinical information (C) TBM adjusted for location, (D) TBM adjusted for location and clinical information. Abbreviations: TBM = transport-based morphometry, CC = correlation co-efficient, σ = standard deviation of the pixel intensity distribution along wcorr, δ = change, mL = milliliters.

Scatter plots showing the relationship between the hematoma image features in the training cohort of the derivation dataset projected onto the most correlated direction wcorr in transport space and change in hematoma volume from the presentation to the 24-hour NCCT scan.

(A) TBM alone, (B) TBM adjusted for clinical information, (C) TBM adjusted for location, (D) TBM adjusted for location and clinical information. Abbreviations: TBM = transport-based morphometry, CC = correlation co-efficient, σ = standard deviation of the pixel intensity distribution along wcorr, δ = change, mL = milliliters.

AUROC analyses of hematoma location as an independent predictor of hematoma expansion in (A) the internal validation cohort and (B) the training cohort of the derivation dataset.

Mean probability distributions of hematoma location projected onto the most discriminant direction w0 in transport space showing the degree of separation between the expansion (red) and no expansion (blue) groups by the learned pLDA classifier boundary in (C) the testing dataset and (D) the training dataset. Abbreviations: AUROC = Area Under the Receiver Operator Curve, ROC = Receiver Operator Curve, pLDA = penalized linear discriminant analysis, σ = standard deviation of the pixel intensity distribution along w0

Mean probability distributions of non-contrast computed tomography hematoma features in the internal validation cohort of the derivation dataset showing the degree of separation between the expansion (red) and no expansion (blue) groups.

(A) Initial hematoma volume, (B) Density heterogeneity (C) Shape eccentricity and (D) Image intensity distribution. Abbreviations: % = per cent, mL = mililiters, HU = Hounsfield Units.

Mean probability distributions of non-contrast computed tomography hematoma features in the training cohort of the derivation dataset showing the degree of separation between the expansion (red) and no expansion (blue) groups.

(A) Initial hematoma volume, (B) Density heterogeneity (C) Shape eccentricity and (D) Image intensity distribution. Abbreviations: % = per cent, mL = mililiters, HU = Hounsfield Units.

AUROC analyses of the performance of each of the visually identified image features as independent predictors of hematoma expansion with comparison to the final TBM model adjusted for location and clinical information in the (A) training cohort of the derivation dataset, (B) internal validation cohort of the training dataset and (C) external validation cohort of the test dataset.

Abbreviations: AUROC = Area Under the Receiver Operator Curve, ROC = Receiver Operator Curve, TBM = transport-based morphometry.

Comparisons of the performance between existing NCCT hematoma expansion prediction scores and the final TBM model adjusted for location and clinical information in the (A) internal validation cohort and (B) training cohort of the derivation dataset.

Hematoma expansion was defined as ≥ 6mL hematoma volume increase from the presentation to the 24±6 hour NCCT scan. Abbreviations: AUROC = Area Under the Receiver Operator Curve, ROC = Receiver Operator Curve, TBM = transport-based morphometry, NCCT = non-contrast computed tomography, Heavn = Heavn score, Brain = Brain score, NAG = NAG scale, PT = 10-point score, BAT = BAT score, TBM = transport-based morphometry.

Forest plots showing the mean correlation co-efficient and corresponding 95% confidence intervals for each of the NCCT expansion prediction scores and 24-hour hematoma growth, measured as change in hematoma volume in mililiters from the presentation to the 24 hour NCCT scan, in the (A) internal validation cohort of the derivation dataset and (B) training cohort of the derivation dataset.

Abbreviations: NCCT = non-contrast computed tomography, Heavn = Heavn score, Brain = Brain score, NAG = NAG scale, PT = 10-point score, BAT = BAT score, TBM = transport-based morphometry.

Comparisons of the performance of alternate machine and deep learning methods and the final TBM model adjusted for location and clinical information in external validation dataset.

Hematoma expansion was defined as ≥ 6mL hematoma volume increase from the presentation to the 24±6-hour NCCT scan. Abbreviations: AUROC = Area Under the Receiver Operator Curve, ROC = Receiver Operator Curve, TBM = transport-based morphometry, NCCT = non-contrast computed tomography, KNN = K-nearest neighbors, SVM = support vector machine, Logistic = logistic regression, ResNet CNN = three dimensional residual networks convolutional neural network.