Automatic estimation of midline shift in patients with cerebral glioma based on enhanced voigt model and local symmetry

Automatic Brain Midline Surface Delineation on 3D CT Images With Intracranial Hemorrhage

Brain midline delineation plays an important role in guiding intracranial hemorrhage surgery, which still remains a challenging task since hemorrhage shifts the normal brain configuration. Most previous studies detected brain midline on 2D plane and did not handle hemorrhage cases well. We propose a novel and efficient hemisphere-segmentation framework (HSF) for 3D brain midline surface delineation. Specifically, we formulate the brain midline delineation as a 3D hemisphere segmentation task, and employ an edge detector and a smooth regularization loss to generate the midline surface. We also introduce a distance-weighted map to keep the attention on the midline. Furthermore, we adopt rectification learning to handle various head poses. Finally, considering the complex situation of ventricle break-in for hemorrhages in bilateral intraventricular (B-IVH) cases, we identify those cases via a classification model and design a midline correction strategy to locally adjust the midline. To our best knowledge, it is the first study focusing on delineating the brain midline surface on 3D CT images of hemorrhage patients and handling the situation of ventricle break-in. Extensive validation on our large in-house datasets (519 patients) and the public CQ500 dataset (491 patients), demonstrates that our method outperforms state-of-the-art methods on brain midline delineation.

Automated Midline Estimation for Symmetry Analysis of Cerebral Hemispheres in FLAIR MRI

To perform brain asymmetry studies in large neuroimaging archives, reliable and automatic detection of the interhemispheric fissure (IF) is needed to first extract the cerebral hemispheres. The detection of the IF is often referred to as mid-sagittal plane estimation, as this plane separates the two cerebral hemispheres. However, traditional planar estimation techniques fail when the IF presents a curvature caused by existing pathology or a natural phenomenon known as brain torque. As a result, midline estimates can be inaccurate. In this study, a fully unsupervised midline estimation technique is proposed that is comprised of three main stages: head angle correction, control point estimation and midline generation. The control points are estimated using a combination of intensity, texture, gradient, and symmetry-based features. As shown, the proposed method automatically adapts to IF curvature, is applied on a slice-to-slice basis for more accurate results and also provides accurate delineation of the midline in the septum pellucidum, which is a source of failure for traditional approaches. The method is compared to two state-of-the-art methods for midline estimation and is validated using 75 imaging volumes (~3,000 imaging slices) acquired from 38 centers of subjects with dementia and vascular disease. The proposed method yields the lowest average error across all metrics: Hausdorff distance (HD) was 0.32 ± 0.23, mean absolute difference (MAD) was 1.10 ± 0.38 mm and volume difference was 7.52 ± 5.40 and 5.35 ± 3.97 ml, for left and right hemispheres, respectively. Using the proposed method, the midline was extracted for 5,360 volumes (~275K images) from 83 centers worldwide, acquired by GE, Siemens and Philips scanners. An asymmetry index was proposed that automatically detected outlier segmentations (which were <1% of the total dataset). Using the extracted hemispheres, hemispheric asymmetry texture biomarkers of the normal-appearing brain matter (NABM) were analyzed in a dementia cohort, and significant differences in biomarker means were found across SCI and MCI and SCI and AD.

Quantitative analysis of brain herniation from non-contrast CT images using deep learning

BACKGROUND

Brain herniation is one of the fatal outcomes of increased intracranial pressure (ICP). It is caused due to the presence of hematoma or tumor mass in the brain. Ideal midline (iML) divides the healthy brain into two (right and left) nearly equal hemispheres. In the presence of hematoma, the midline tends to shift from its original position to the contralateral side of the mass and thus develops a deformed midline (dML).

NEW METHOD

In this study, a convolutional neural network (CNN) was used to predict the deformed left and right hemispheres. The proposed algorithm was validated with non-contrast computed tomography (NCCT) of (n = 45) subjects with two types of brain hemorrhages - epidural hemorrhage (EDH): (n = 5) and intra-parenchymal hemorrhage (IPH): (n = 40)).

RESULTS

The method demonstrated excellent potential in automatically predicting MLS with the average errors of 1.29 mm by location, 66.4 mm² by 2D area, and 253.73 mm³ by 3D volume. Estimated MLS could be well correlated with other clinical markers including hematoma volume - R² = 0.86 (EDH); 0.48 (IPH) and a Radiologist-defined severity score (RSS) - R² = 0.62 (EDH); 0.57 (IPH). RSS was found to be even better correlated (R² = 0.98 (EDH); 0.70 (IPH)), hence better predictable by a joint correlation between hematoma volume, midline pixel- or voxel-shift, and minimum distance of (ideal or deformed) midline from the hematoma (boundary or centroid).

CONCLUSION

All these predictors were computed automatically, which highlighted the excellent clinical potential of the proposed automated method in midline shift (MLS) estimation and severity prediction in hematoma decision support systems.

The delineation of largely deformed brain midline using regression-based line detection network

PURPOSE

The human brain has two cerebral hemispheres that are roughly symmetric and separated by a midline, which is nearly a straight line shown in axial computed tomography (CT) images in healthy subjects. However, brain diseases such as hematoma and tumors often cause midline shift, where the degree of shift can be regarded as a quantitative indication in clinical practice. To facilitate clinical evaluation, we need computer-aided methods to automate this quantification. Nevertheless, most existing studies focused on the landmark- or symmetry-based methods that provide only the existence of shift or its maximum distance, which could be easily affected by anatomical variability and large brain deformations. Intuitive results such as midline delineation or measurement are lacking. In this study, we focus on developing an automated and robust method based on the fully convolutional neural network for the delineation of midline in largely deformed brains.

METHODS

We propose a novel regression-based line detection network (RLDN) for the robust midline delineation, especially in largely deformed brains. Specifically, to improve the robustness of delineation in largely deformed brains, we regard the delineation of the midline as the skeleton extraction task and then use the multiscale bidirectional integration module to acquire more representative features. Based on the skeleton extraction, we incorporate the regression task into it to delineate more accurate and continuous midline, especially in largely deformed brains. Our study utilized the public CQ 500 dataset (128 subjects) for training with hold-out validation on 61 subjects from a private cohort accrued from a local hospital.

RESULTS

The mean line distance error and F1-score were 1.17 ± 0.72 mm with 0.78 on CQ 500 test set, and 4.15 ± 3.97 mm with 0.61 on the private dataset. Besides, significant differences (P < 0.05) were observed between our method and other comparative ones on these two datasets.

CONCLUSIONS

This work provides a novel solution to acquire robust delineation of the midline, especially in largely deformed brains, and achieves state-of-the-art performance on the public and our private dataset, which makes it possible for automated diagnosis of relevant brain diseases in the future.

Automatic Quantification of Computed Tomography Features in Acute Traumatic Brain Injury

Traumatic brain injury is a complex and diverse medical condition with a high frequency of intracranial abnormalities. These can typically be visualized on a computed tomography (CT) scan, which provides important information for further patient management, such as the need for operative intervention. In order to quantify the extent of acute intracranial lesions and associated secondary injuries, such as midline shift and cisternal compression, visual assessment of CT images has limitations, including observer variability and lack of quantitative interpretation. Automated image analysis can quantify the extent of intracranial abnormalities and provide added value in routine clinical practice. In this article, we present icobrain, a fully automated method that reliably computes acute intracranial lesions volume based on deep learning, cistern volume, and midline shift on the noncontrast CT image of a patient. The accuracy of our method is evaluated on a subset of the multi-center data set from the CENTER-TBI (Collaborative European Neurotrauma Effectiveness Research in Traumatic Brain Injury) study for which expert annotations were used as a reference. Median volume differences between expert assessments and icobrain are 0.07 mL for acute intracranial lesions and -0.01 mL for cistern segmentation. Correlation between expert assessments and icobrain is 0.91 for volume of acute intracranial lesions and 0.94 for volume of the cisterns. For midline shift computations, median error is -0.22 mm, with a correlation of 0.93 with expert assessments.

Brain Midline Shift Measurement and Its Automation: A Review of Techniques and Algorithms

Midline shift (MLS) of the brain is an important feature that can be measured using various imaging modalities including X-ray, ultrasound, computed tomography, and magnetic resonance imaging. Shift of midline intracranial structures helps diagnosing intracranial lesions, especially traumatic brain injury, stroke, brain tumor, and abscess. Being a sign of increased intracranial pressure, MLS is also an indicator of reduced brain perfusion caused by an intracranial mass or mass effect. We review studies that used the MLS to predict outcomes of patients with intracranial mass. In some studies, the MLS was also correlated to clinical features. Automated MLS measurement algorithms have significant potentials for assisting human experts in evaluating brain images. In symmetry-based algorithms, the deformed midline is detected and its distance from the ideal midline taken as the MLS. In landmark-based ones, MLS was measured following identification of specific anatomical landmarks. To validate these algorithms, measurements using these algorithms were compared to MLS measurements made by human experts. In addition to measuring the MLS on a given imaging study, there were newer applications of MLS that included comparing multiple MLS measurement before and after treatment and developing additional features to indicate mass effect. Suggestions for future research are provided.

Post-Surgery Glioma Growth Modeling from Magnetic Resonance Images for Patients with Treatment

Reaction diffusion is the most common growth modelling methodology due to its simplicity and consistency with the biological tumor growth process. However, current extensions of the reaction diffusion model lack one or more of the following: efficient inclusion of treatments' effects, taking into account the viscoelasticity of brain tissues, and guaranteed stability of the numerical solution. We propose a new model to overcome the aforementioned drawbacks. Guided by directional information derived from diffusion tensor imaging, our model relates tissue heterogeneity with the absorption of the chemotherapy, adopts the linear-quadratic term to simulate the radiotherapy effect, employs Maxwell-Weichert model to incorporate brain viscoelasticity, and ensures the stability of the numerical solution. The performance is verified through experiments on synthetic and real MR images. Experiments on 9 MR datasets of patients with low grade gliomas undergoing surgery with different treatment regimens are carried out and validated using Jaccard score and Dice coefficient. The growth simulation accuracies of the proposed model are in ranges of [0.673 0.822] and [0.805 0.902] for Jaccard scores and Dice coefficients, respectively. The accuracies decrease up to 4% and 2.4% when ignoring treatment effects and the tensor information, while brain viscoelasticity has no significant impact on the accuracies.

Web of Science, Scopus, and Google Scholar citation rates: a case study of medical physics and biomedical engineering: what gets cited and what doesn't?

There are often differences in a publication's citation count, depending on the database accessed. Here, aspects of citation counts for medical physics and biomedical engineering papers are studied using papers published in the journal Australasian physical and engineering sciences in medicine. Comparison is made between the Web of Science, Scopus, and Google Scholar. Papers are categorised into subject matter, and citation trends are examined. It is shown that review papers as a group tend to receive more citations on average; however the highest cited individual papers are more likely to be research papers.

Volatile Oil of Acori Graminei Rhizoma-Induced Apoptosis and Autophagy are dependent on p53 Status in Human Glioma Cells

Acori Graminei Rhizoma is well known for the beneficial effects on CNS disorders in traditional medicine. Though it is frequently prescribed in formulations for brain tumors, the anti-glioma effect has not been examined. We used volatile oil of Acori Graminei Rhizoma (VOA) and human glioblastoma multiforme (GBM) cells in this study. We found that VOA exhibited greater growth suppression in p53 wild-type cells than p53 mutant cells and very low effect on fibroblasts and human glial HEB cells. Apoptosis was triggered by VOA with a caspase-dependent way in p53 wild-type A172 cells, while a caspase-independent way in p53 mutant U251 cells. Meanwhile, both A172 and U251 cells treated by VOA displayed autophagic features. Furthermore, p53 decrease was observed along with VOA-induced apoptosis and autophagy in A172 cells. VOA-induced autophagy was mediated through a p53/AMPK/mTOR signaling pathway in A172 cells, while an mTOR-independent signaling pathway in U251 cells. Finally, blockage of autophagy potentiated the proapoptotic effect in both A172 and U251 cells, indicating a protective role of autophagy in VOA-induced cell death. Together, VOA exhibited anti-tumor activity in human GBM cells and induced apoptotic cell death and protective autophagy, which is cell type specific and dependent on p53 status.