Multiclass semantic segmentation and quantification of traumatic brain injury lesions on head CT using deep learning: an algorithm development and multicentre validation study

Background: Computed tomography (CT) is the most common imaging modality in traumatic brain injury (TBI). However, its conventional use requires expert clinical interpretation and does not provide detailed quantitative outputs, which may have prognostic importance. Deep learning could reliably and efficiently detect, distinguish, and quantify different lesion types, providing opportunities for personalised treatment strategies and clinical research. Methods: An initial convolutional neural network (CNN) was trained and validated on expert manual segmentations (97 scans). This CNN was then used to automatically segment a new set of 839 scans, which were then manually corrected by experts. From these, a subset of 184 scans was used to train a final CNN for multi-class, voxel-wise segmentation of lesion types. The performance of this CNN was evaluated on a held-out test set with 655 scans. External validation was performed on a large, independent set of 500 patients from a different continent. Findings: When compared to manual reference, CNN-derived lesion volumes showed a mean error of 0·86mL (95% CI -5·23 to 6·94) for intraparenchymal haemorrhage (IPH), 1·83mL (-12·01 to 15·66) for extra-axial haemorrhage (EAH), 2·09mL (-9·38 to 13·56) for perilesional oedema and 0·07mL (-1·00 to 1·13) for intraventricular haemorrhage (IVH). Further, the CNN detected lesions with AUCs of 0·90 (0·86-0·94) for IPH, 0·80 (0·75-0·85) for EAH, 0·95 (0·89-1·00) for IVH on the external, independent patient dataset. Interpretation: We demonstrate the ability of a CNN to separately segment, detect and quantify multi-class haemorrhagic lesions and importantly, perilesional oedema. These volumetric lesion estimates allow clinically relevant quantification of lesion burden and progression, with potential applications in clinical care and research in TBI. Funding: European Union 7th Framework Programme, Hannelore Kohl Stiftung; OneMind; Integra Neurosciences; European Research Council Horizon 2020; Engineering and Physical Sciences Research Council (UK); Academy of Medical Sciences/Health Foundation (UK); National Institute for Health Research (UK).CENTER-TBI study was supported by the European Union 7th Framework program (EC grant 602150). Additional funding sources: Hannelore Kohl Stiftung; NeuroTrauma Sciences; Integra Neurosciences; European Research Council (ERC) Horizon 2020 (EC grant 757173); Engineering and Physical Sciences Research Council (EPSRC) (EP/R511547/1); Academy of Medical Sciences/The Health Foundation (UK); National Institute for Health Research (UK)

Diagnostic performance of android-based handheld device using endeavor mobile application in interpretation of a traumatic non- contrasted computed tomography (CT) brain

Title: Diagnostic Performance of Android-Based Handheld Device Using Endeavor Mobile Application in Interpretation of a Traumatic Non-Contrasted Computed Tomography (CT) Brain Purpose: To determine the image viewing quality of the handheld device of Android premium devices and investigate the usage of handheld device in interpretation of CT images of trauma cases. Materials and methods: Using Samsung Galaxy Note 10.1, we installed the AAPM TG-18 QC test pattern and recruited 30 candidates to do a subjective review of the QC test pattern on both handheld and workstation monitors. As for the investigation of using handheld device to interpret CT images of trauma cases, we recruited 2 observers, consisting of a radiologist and a final year resident to review 180 cases of CT brain.Their findings would then be cross-referenced to a result obtained by a consultant radiologist using workstation monitor. Kappa test were used to calculate the Interobserver agreement. Results: There was 100% reproducibility of the same level of luminance patches, grayscale continuation and spatial resolution in the handheld device when compared to the workstation monitor. Other components investigated produced similar results when the candidates were allowed to zoom in and change the window settings. The sensitivity of detecting lesions on the images using handheld devices ranging from 50.0% to 84.6%. The negative predictive values were generally high indicating that the handheld device has high accuracy to determine absence of certain lesion. There was substantial agreement regarding the findings of both observer compared to gold standard test on workstation monitor. Conclusions: Current generation of handheld devices has had viewing quality at par with workstation monitor and it is a reliable tool in reviewing CT for trauma cases

Automatic Reporting of TBI Lesion Location in CT based on Deep Learning and Atlas Mapping

Tese de mestrado integrado, Engenharia Biomédica e Biofísica (Biofísica Médica e Fisiologia de Sistemas), 2021, Universidade de Lisboa, Faculdade de CiênciasThe assessment of Computed Tomography (CT) scans for Traumatic Brain Injury (TBI) management remains a time consuming and challenging task for physicians. Computational methods for quantitative lesion segmentation and localisation may increase consistency in diagnosis and prognosis criteria. Our goal was to develop a registration-based tool to accurately localise several lesion classes (i.e., calculate the volume of lesion per brain region), as an extension of the Brain Lesion Analysis and Segmentation Tool for CT (BLAST-CT). Lesions were located by projecting a Magnetic Resonance Imaging (MRI) labelled atlas from the Montreal Neurological Institute (MNI MRI atlas) to a lesion map in native space. We created a CT template to work as an intermediate step between the two imaging spaces, using 182 non-lesioned CT scans and an unbiased iterative registration approach. We then non-linearly registered the parcellated atlas to the CT template, subsequently registering (affine) the result to native space. From the final atlas alignment, it was possible to calculate the volume of each lesion class per brain region. This pipeline was validated on a multi-centre dataset (n=839 scans), and defined three methods to flag any scans that presented sub-optimal results. The first one was based on the similarity metric of the registration of every scan to the study-specific CT template, the second aimed to identify any scans with regions that were completely collapsed post registration, and the final one identified scans with a significant volume of intra-ventricular haemorrhage outside of the ventricles. Additionally, an assessment of lesion prevalence and of the false negative and false positive rates of the algorithm, per anatomical region, was conducted, along with a bias assessment of the BLAST-CT tool. Our results show that the constructed pipeline is able to successfully localise TBI lesions across the whole brain, although without voxel-wise accuracy. We found the error rates calculated for each brain region to be inversely correlated with the lesion volume within that region. No considerable bias was identified on BLAST-CT, as all the significant correlation coefficients calculated between the Dice scores and clinical variables (i.e., age, Glasgow Coma Scale, Extended Glasgow Outcome Scale and Injury Severity Score) were below 0.2. Our results also suggest that the variation in DSC between male and female patients within a specific age range was caused by the discrepancy in lesion volume presented by the scans included in each sample

Relationship of admission blood proteomic biomarkers levels to lesion type and lesion burden in traumatic brain injury: A CENTER-TBI study.

BACKGROUND: We aimed to understand the relationship between serum biomarker concentration and lesion type and volume found on computed tomography (CT) following all severities of TBI. METHODS: Concentrations of six serum biomarkers (GFAP, NFL, NSE, S100B, t-tau and UCH-L1) were measured in samples obtained <24 hours post-injury from 2869 patients with all severities of TBI, enrolled in the CENTER-TBI prospective cohort study (NCT02210221). Imaging phenotypes were defined as intraparenchymal haemorrhage (IPH), oedema, subdural haematoma (SDH), extradural haematoma (EDH), traumatic subarachnoid haemorrhage (tSAH), diffuse axonal injury (DAI), and intraventricular haemorrhage (IVH). Multivariable polynomial regression was performed to examine the association between biomarker levels and both distinct lesion types and lesion volumes. Hierarchical clustering was used to explore imaging phenotypes; and principal component analysis and k-means clustering of acute biomarker concentrations to explore patterns of biomarker clustering. FINDINGS: 2869 patient were included, 68% (n=1946) male with a median age of 49 years (range 2-96). All severities of TBI (mild, moderate and severe) were included for analysis with majority (n=1946, 68%) having a mild injury (GCS 13-15). Patients with severe diffuse injury (Marshall III/IV) showed significantly higher levels of all measured biomarkers, with the exception of NFL, than patients with focal mass lesions (Marshall grades V/VI). Patients with either DAI+IVH or SDH+IPH+tSAH, had significantly higher biomarker concentrations than patients with EDH. Higher biomarker concentrations were associated with greater volume of IPH (GFAP, S100B, t-tau;adj r2 range:0·48-0·49; p<0·05), oedema (GFAP, NFL, NSE, t-tau, UCH-L1;adj r2 range:0·44-0·44; p<0·01), IVH (S100B;adj r2 range:0.48-0.49; p<0.05), Unsupervised k-means biomarker clustering revealed two clusters explaining 83·9% of variance, with phenotyping characteristics related to clinical injury severity. INTERPRETATION: Interpretation: Biomarker concentration within 24 hours of TBI is primarily related to severity of injury and intracranial disease burden, rather than pathoanatomical type of injury. FUNDING: CENTER-TBI is funded by the European Union 7th Framework programme (EC grant 602150)

Advancing probabilistic and causal deep learning in medical image analysis

The power and flexibility of deep learning have made it an indispensable tool for tackling modern machine learning problems. However, this flexibility comes at the cost of robustness and interpretability, which can lead to undesirable or even harmful outcomes. Deep learning models often fail to generalise to real-world conditions and produce unforeseen errors that hinder wide adoption in safety-critical critical domains such as healthcare. This thesis presents multiple works that address the reliability problems of deep learning in safety-critical domains by being aware of its vulnerabilities and incorporating more domain knowledge when designing and evaluating our algorithms. We start by showing how close collaboration with domain experts is necessary to achieve good results in a real-world clinical task - the multiclass semantic segmentation of traumatic brain injuries (TBI) lesions in head CT. We continue by proposing an algorithm that models spatially coherent aleatoric uncertainty in segmentation tasks by considering the dependencies between pixels. The lack of proper uncertainty quantification is a robustness issue which is ubiquitous in deep learning. Tackling this issue is of the utmost importance if we want to deploy these systems in the real world. Lastly, we present a general framework for evaluating image counterfactual inference models in the absence of ground-truth counterfactuals. Counterfactuals are extremely useful to reason about models and data and to probe models for explanations or mistakes. As a result, their evaluation is critical for improving the interpretability of deep learning models.Open Acces

Relationship of admission blood proteomic biomarkers levels to lesion type and lesion burden in traumatic brain injury: A CENTER-TBI study.

BACKGROUND: We aimed to understand the relationship between serum biomarker concentration and lesion type and volume found on computed tomography (CT) following all severities of TBI. METHODS: Concentrations of six serum biomarkers (GFAP, NFL, NSE, S100B, t-tau and UCH-L1) were measured in samples obtained <24 hours post-injury from 2869 patients with all severities of TBI, enrolled in the CENTER-TBI prospective cohort study (NCT02210221). Imaging phenotypes were defined as intraparenchymal haemorrhage (IPH), oedema, subdural haematoma (SDH), extradural haematoma (EDH), traumatic subarachnoid haemorrhage (tSAH), diffuse axonal injury (DAI), and intraventricular haemorrhage (IVH). Multivariable polynomial regression was performed to examine the association between biomarker levels and both distinct lesion types and lesion volumes. Hierarchical clustering was used to explore imaging phenotypes; and principal component analysis and k-means clustering of acute biomarker concentrations to explore patterns of biomarker clustering. FINDINGS: 2869 patient were included, 68% (n=1946) male with a median age of 49 years (range 2-96). All severities of TBI (mild, moderate and severe) were included for analysis with majority (n=1946, 68%) having a mild injury (GCS 13-15). Patients with severe diffuse injury (Marshall III/IV) showed significantly higher levels of all measured biomarkers, with the exception of NFL, than patients with focal mass lesions (Marshall grades V/VI). Patients with either DAI+IVH or SDH+IPH+tSAH, had significantly higher biomarker concentrations than patients with EDH. Higher biomarker concentrations were associated with greater volume of IPH (GFAP, S100B, t-tau;adj r2 range:0·48-0·49; p<0·05), oedema (GFAP, NFL, NSE, t-tau, UCH-L1;adj r2 range:0·44-0·44; p<0·01), IVH (S100B;adj r2 range:0.48-0.49; p<0.05), Unsupervised k-means biomarker clustering revealed two clusters explaining 83·9% of variance, with phenotyping characteristics related to clinical injury severity. INTERPRETATION: Interpretation: Biomarker concentration within 24 hours of TBI is primarily related to severity of injury and intracranial disease burden, rather than pathoanatomical type of injury. FUNDING: CENTER-TBI is funded by the European Union 7th Framework programme (EC grant 602150)

Development of a lesion localisation tool to improve outcome prediction in Traumatic Brain Injury patients

Tese de mestrado integrado, Engenharia Biomédica e Biofísica (Engenharia Clínica e Instrumentação Médica) Universidade de Lisboa, Faculdade de Ciências, 2022Traumatic brain injury (TBI) is a highly heterogeneous pathology that poses severe health and socioeconomic problems on a global scale. Neuroimaging research and development has advanced its clinical care in numerous ways, as injured brains are being imaged and studied in greater detail. The size and location of TBI lesions are often necessary to accurately determine a prognosis, which is key in defining a patient-specific rehabilitation program. This dissertation aims to investigate the impact of lesion characteristics, such as volume and location, on outcome prediction in TBI patients. Lesion localisation was achieved by comparing annotated TBI lesions to a brain atlas. Furthermore, other lesion characteristics were examined across different Magnetic Resonance Imaging (MRI) sequences and scanners, with results suggesting that the use of different scanners or MRI contrasts introduced biases in said lesion characteristics. Patient outcome was predicted using four generalised linear models. Besides clinical variables, these models included lesion volume, group and location as predictors. Model comparison indicated that lesion volume could be beneficial for outcome prediction, but may be dependent on both lesion group and location. Overall, this methodology showed potential in uncovering the effect that certain lesion groups and/or locations have on patient outcome after TBI