Segmentation Of Cerebrospinal Fluid Research Articles

Highfield imaging of the subgenual anterior cingulate cortex in uni- and bipolar depression.

The subgenual Anterior Cingulate Cortex (sgACC), as a part of the Anterior Cingulate Cortex and the limbic system plays a crucial role in mood regulation. Previous structural and functional brain imaging studies of the sgACC have revealed alterations of Gray Matter (GM) volumes and Blood Oxygenation Level Dependent signals (BOLD) in patients with Major Depressive Disorder (MDD) and Bipolar Disorder (BD), suggesting potential biomarker traits for affective disorders. In this study we investigated the gray matter volume of the sgACC in 3 different patient groups: 40 MDD patients, of which 20 were medicated (MDDm) and 20 were unmedicated (MDDu), and 21 medicated BD patients, and compared them with 23 healthy volunteers. We examined GM volume alteration using high-resolution 7T Magnetic Resonance Imaging (MRI) which produced quantitative maps of the spin-lattice relaxation time (T1). T1 maps provide high contrast between gray and white matter, and at 7 Tesla voxels with submillimeter resolution can be acquired in a reasonable scan time. We developed a semi-automatic segmentation protocol based on refined landmarks derived from previous volumetric studies using quantitative T1 maps as raw input data for automatic tissue segmentation of GM, WM and CSF (cerebrospinal fluid) tissue. The sgACC ROI was then superimposed on these tissue probability maps and traced manually by two independent raters (F.B., M.M.) following our semi-automatic segmentation protocol. Interrater reliability was calculated on a subset of 10 brain scans for each hemisphere, showing an Intra-Class Correlation coefficient (ICC) r = 0.96 for left sgACC and r = 0.84 for right sgACC respectively. In summary, we have developed a reproducible and reliable semi-automatic segmentation protocol to measure gray matter volume in the sgACC. Based on previous findings from meta-analyses on morphometric studies of the sgACC, we hypothesized that patients with MDD would have lower gray matter sgACC volumes compared to healthy subjects. Post-hoc analysis revealed smaller subgenual volumes for the left hemisphere in both the medicated (MDDm) and non-medicated (MDDu) group versus healthy controls (p = .001, p = .008) respectively. For the right hemisphere, the (MDDu) and BD group exhibited significantly lower subgenual volumes than healthy controls (p < .001, p = .004) respectively. To our knowledge, this is the first morphometric MRI study using T1 maps obtained in high-resolution 7 Tesla MRI to compare MDD and BD patients with healthy controls.

Automated volumetric evaluation of intracranial compartments and cerebrospinal fluid distribution on emergency trauma head CT scans to quantify mass effect.

Intracranial space is divided into three compartments by the falx cerebri and tentorium cerebelli. We assessed whether cerebrospinal fluid (CSF) distribution evaluated by a specifically developed deep-learning neural network (DLNN) could assist in quantifying mass effect. Head trauma CT scans from a high-volume emergency department between 2018 and 2020 were retrospectively analyzed. Manual segmentations of intracranial compartments and CSF served as the ground truth to develop a DLNN model to automate the segmentation process. Dice Similarity Coefficient (DSC) was used to evaluate the segmentation performance. Supratentorial CSF Ratio was calculated by dividing the volume of CSF on the side with reduced CSF reserve by the volume of CSF on the opposite side. Two hundred and seventy-four patients (mean age, 61 years ± 18.6) after traumatic brain injury (TBI) who had an emergency head CT scan were included. The average DSC for training and validation datasets were respectively: 0.782 and 0.765. Lower DSC were observed in the segmentation of CSF, respectively 0.589, 0.615, and 0.572 for the right supratentorial, left supratentorial, and infratentorial CSF regions in the training dataset, and slightly lower values in the validation dataset, respectively 0.567, 0.574, and 0.556. Twenty-two patients (8%) had midline shift exceeding 5 mm, and 24 (8.8%) presented with high/mixed density lesion exceeding >25 ml. Fifty-five patients (20.1%) exhibited mass effect requiring neurosurgical treatment. They had lower supratentorial CSF volume and lower Supratentorial CSF Ratio (both p < 0.001). A Supratentorial CSF Ratio below 60% had a sensitivity of 74.5% and specificity of 87.7% (AUC 0.88, 95%CI 0.82-0.94) in identifying patients that require neurosurgical treatment for mass effect. On the other hand, patients with CSF constituting 10-20% of the intracranial space, with 80-90% of CSF specifically in the supratentorial compartment, and whose Supratentorial CSF Ratio exceeded 80% had minimal risk. CSF distribution may be presented as quantifiable ratios that help to predict surgery in patients after TBI. Automated segmentation of intracranial compartments using the DLNN model demonstrates a potential of artificial intelligence in quantifying mass effect. Further validation of the described method is necessary to confirm its efficacy in triaging patients and identifying those who require neurosurgical treatment.

Effects of atlas-based anatomy on modelled light transport in the neonatal head

Objective. Diffuse optical tomography (DOT) provides a relatively convenient method for imaging haemodynamic changes related to neuronal activity on the cerebral cortex. Due to practical challenges in obtaining anatomical images of neonates, an anatomical framework is often created from an age-appropriate atlas model, which is individualized to the subject based on measurements of the head geometry. This work studies the approximation error arising from using an atlas instead of the neonate's own anatomical model. Approach. We consider numerical simulations of frequency-domain (FD) DOT using two approaches, Monte Carlo simulations and diffusion approximation via finite element method, and observe the variation in (1) the logarithm of amplitude and phase shift measurements, and (2) the corresponding inner head sensitivities (Jacobians), due to varying segmented anatomy. Varying segmentations are sampled by registering 165 atlas models from a neonatal database to the head geometry of one individual selected as the reference model. Prior to the registration, we refine the segmentation of the cerebrospinal fluid (CSF) by separating the CSF into two physiologically plausible layers. Main results. In absolute measurements, a considerable change in the grey matter or extracerebral tissue absorption coefficient was found detectable over the anatomical variation. In difference measurements, a small local 10%-increase in brain absorption was clearly detectable in the simulated measurements over the approximation error in the Jacobians, despite the wide range of brain maturation among the registered models. Significance. Individual-level atlas models could potentially be selected within several weeks in gestational age in DOT difference imaging, if an exactly age-appropriate atlas is not available. The approximation error method could potentially be implemented to improve the accuracy of atlas-based imaging. The presented CSF segmentation algorithm could be useful also in other model-based imaging modalities. The computation of FD Jacobians is now available in the widely-used Monte Carlo eXtreme software.

An advanced fuzzy C-Means algorithm for the tissue segmentation from brain magnetic resonance images in the presence of noise and intensity inhomogeneity

ABSTRACT Segmentation of brain Magnetic Resonance Images (MRIs) into various brain tissues such as white matter (WM), grey matter (GM), and cerebrospinal fluid (CSF) is very important to detect and diagnose different brain-related disorders at the primitive level. Accurate segmentation of brain MRIs is very difficult because of the intricate anatomical structure of the tissues, the existence of Intensity Inhomogeneity (IIH), noise, and Partial Volume Effects (PVE). Clustering-based methods are generally used to segment brain images. This work proposes a Chaotic based Enhanced Firefly Algorithm Integrated with Fuzzy C-Means (CEFAFCM) for the segmentation of brain tissues WM, GM, and CSF from brain MRIs. The proposed method can handle IIH, PVE, and noise. CEFAFCM is a spatially modified FCM algorithm combined with the Firefly Algorithm (FA) along with a chaotic map for the initialization of the population of fireflies. The algorithm is tested with brain MRIs acquired from the BrainWeb database. The experimental results demonstrate that the proposed technique is producing better results in comparison with some existing brain MRI segmentation methods such as FCM, BCFCM, FAFCM, and En-FAFCM.

A domain adaptation benchmark for T1-weighted brain magnetic resonance image segmentation.

Accurate brain segmentation is critical for magnetic resonance imaging (MRI) analysis pipelines. Machine-learning-based brain MR image segmentation methods are among the state-of-the-art techniques for this task. Nevertheless, the segmentations produced by machine learning models often degrade in the presence of expected domain shifts between the test and train sets data distributions. These domain shifts are expected due to several factors, such as scanner hardware and software differences, technology updates, and differences in MRI acquisition parameters. Domain adaptation (DA) methods can make machine learning models more resilient to these domain shifts. This paper proposes a benchmark for investigating DA techniques for brain MR image segmentation using data collected across sites with scanners from different vendors (Philips, Siemens, and General Electric). Our work provides labeled data, publicly available source code for a set of baseline and DA models, and a benchmark for assessing different brain MR image segmentation techniques. We applied the proposed benchmark to evaluate two segmentation tasks: skull-stripping; and white-matter, gray-matter, and cerebrospinal fluid segmentation, but the benchmark can be extended to other brain structures. Our main findings during the development of this benchmark are that there is not a single DA technique that consistently outperforms others, and hyperparameter tuning and computational times for these methods still pose a challenge before broader adoption of these methods in the clinical practice.

Quantitative Measurement of Spinal Cerebrospinal Fluid by Cascade Artificial Intelligence Models in Patients with Spontaneous Intracranial Hypotension.

Cerebrospinal fluid (CSF) hypovolemia is the core of spontaneous intracranial hypotension (SIH). More than 1000 magnetic resonance myelography (MRM) images are required to evaluate each subject. An effective spinal CSF quantification method is needed. In this study, we proposed a cascade artificial intelligence (AI) model to automatically segment spinal CSF. From January 2014 to December 2019, patients with SIH and 12 healthy volunteers (HVs) were recruited. We evaluated the performance of AI models which combined object detection (YOLO v3) and semantic segmentation (U-net or U-net++). The network of performance was evaluated using intersection over union (IoU). The best AI model was used to quantify spinal CSF in patients. We obtained 25,603 slices of MRM images from 13 patients and 12 HVs. We divided the images into training, validation, and test datasets with a ratio of 4:1:5. The IoU of Cascade YOLO v3 plus U-net++ (0.9374) was the highest. Applying YOLO v3 plus U-net++ to another 13 SIH patients showed a significant decrease in the volume of spinal CSF measured (59.32 ± 10.94 mL) at disease onset compared to during their recovery stage (70.61 ± 15.31 mL). The cascade AI model provided a satisfactory performance with regard to the fully automatic segmentation of spinal CSF from MRM images. The spinal CSF volume obtained through its measurements could reflect a patient’s clinical status.

Uncertainty quantification of TMS simulations considering MRI segmentation errors

Objective. Transcranial magnetic stimulation (TMS) is a non-invasive brain stimulation method that is used to study brain function and conduct neuropsychiatric therapy. Computational methods that are commonly used for electric field (E-field) dosimetry of TMS are limited in accuracy and precision because of possible geometric errors introduced in the generation of head models by segmenting medical images into tissue types. This paper studies E-field prediction fidelity as a function of segmentation accuracy. Approach.The errors in the segmentation of medical images into tissue types are modeled as geometric uncertainty in the shape of the boundary between tissue types. For each tissue boundary realization, we then use an in-house boundary element method to perform a forward propagation analysis and quantify the impact of tissue boundary uncertainties on the induced cortical E-field. Main results. Our results indicate that predictions of E-field induced in the brain are negligibly sensitive to segmentation errors in scalp, skull and white matter (WM), compartments. In contrast, E-field predictions are highly sensitive to possible cerebrospinal fluid (CSF) segmentation errors. Specifically, the segmentation errors on the CSF and gray matter interface lead to higher E-field uncertainties in the gyral crowns, and the segmentation errors on CSF and WM interface lead to higher uncertainties in the sulci. Furthermore, the uncertainty of the average cortical E-fields over a region exhibits lower uncertainty relative to point-wise estimates. Significance. The accuracy of current cortical E-field simulations is limited by the accuracy of CSF segmentation accuracy. Other quantities of interest like the average of the E-field over a cortical region could provide a dose quantity that is robust to possible segmentation errors.

Rubik-Net: Learning Spatial Information via Rotation-Driven Convolutions for Brain Segmentation.

The accurate segmentation of brain tissue in Magnetic Resonance Image (MRI) slices is essential for assessing neurological conditions and brain diseases. However, it is challenging to segment MRI slices because of the low contrast between different brain tissues and the partial volume effect. 2-Dimensional (2-D) convolutional networks cannot handle such volumetric image data well because they overlook spatial information between MRI slices. Although 3-Dimensional (3-D) convolutions capture volumetric spatial information, they have not been fully exploited to enhance representative ability of deep networks; moreover, they may lead to overfitting with insufficient training data. In this paper, we propose a novel convolutional mechanism, termed Rubik convolution, to capture multi-dimensional information between MRI slices. Rubik convolution rotates the axis of a set of consecutive slices, enabling 2-D convolution kernels to extract features of each axial plane simultaneously. Next, feature maps are rotated back to fuse multidimensional information by the Max-View-Maps. Furthermore, we propose an efficient 2-D convolutional network, namely Rubik-Net, where the residual connections and the bottleneck structure are used to enhance information transmission and reduce the number of network parameters. The Rubik-Net shows promising results on iSeg2017, iSeg2019, IBSR and BrainWeb datasets in terms of segmentation accuracy. In particular, we achieved the best results in 95th percentile Hausdorff distance and average surface distance in cerebrospinal fluid segmentation on the most challenging iSeg2019 dataset. The experiments indicate that Rubik-Net improves the accuracy and efficiency of medical image segmentation. Moreover, Rubik convolution can be easily embedded into existing 2-D convolutional networks.

White Matter, Gray Matter and Cerebrospinal Fluid Segmentation from Brain Magnetic Resonance Imaging Using Adaptive U-Net and Local Convolutional Neural Network

Abstract According to the World Alzheimer Report 2015, 46 million people are living with dementia in the world. The diagnosis of diseases helps doctors treating patients better. One of the signs of diseases is related to white matter, grey matter and cerebrospinal fluid. Therefore, the automatic segmentation of three tissues in brain imaging especially from magnetic resonance imaging (MRI) plays an important role in medical analysis. In this research, we proposed an effective approach to segment automatically these tissues in three-dimensional (3D) brain MRI. First, a deep learning model is used to segment the sure and unsure regions. In the unsure region, another deep learning model is used to classify each pixel. In the experiments, an adaptive U-net model is used to segment the sure and unsure regions, and the Local Convolutional Neural Network (CNN) model with multiple inputs is used to classify each pixel only in the unsure region. Our method was evaluated with a real image database, Internet Brain Segmentation Repository database, with 18 persons (IBSR 18) (https://www.nitrc.org/projects/ibsr) and compared with state of art methods being the results very promising.

Segmentation of WM, GM, and CSF from the Brain MRIs Using an Advanced Metaheuristic Approach

Segmentation of brain tissues from the Magnetic Resonance Image (MRI) is vital to detect and diagnose brain-related diseases. Precise brain MRI segmentation is challenging because of the unpredictable anatomical structure of the brain, existence of Intensity Inhomogeneity (IIH), Partial Volume (PV) effects, and noise. Most of the brain disorders can be identified exactly by the segmentation of brain tissues, such as White Matter (WM), Gray Matter (GM), and Cerebrospinal Fluid (CSF). The optimization algorithmic approach is widely used for the brain image segmentation. In this work, a robust metaheuristic Multi-level Thresholding (MT) technique named as Advanced Wind Driven Optimization (AWDO) is employed for the brain MRI segmentation. The optimal MT values can be attained by combining the AWDO with the very popular Otsu’s objective function. The results obtained by the proposed AWDO in segmenting various brain images at an optimized value of threshold are compared with the groundtruths and have given the high values for the metrics such as sensitivity, specificity, segmentation accuracy, dice coefficient, and Precision. The values of the objective function, Peak Signal to Noise Ratio (PSNR), Standard Deviation (SD), and Convergence time of the proposed method are also being obtained at various threshold levels. The results are compared with the results of some existing algorithms like Multiplicative Intrinsic Component Optimization (MICO), Electromagnetism Optimization (EMO), Histogram based Darwinian Particle Swarm Optimization (HDPSO), Fully Convolutional Neural Networks (FCNN), and Advance Ant Colony Optimization (AACO). The results justified the notable performance of the proposed approach in segmenting different brain tissues.

Abstract 3071: A method for infratentorial structures segmentation for tumor treating fields treatment planning

Abstract Introduction: Tumor treating fields (TTFields) is an FDA approved treatment for the management of glioblastoma multiform (GBM) and mesothelioma (MPM) and is associated with a significant extension to patients' survival. We have developed software to optimize the location of the TTFields transducer arrays (TAs) on GBM patients' head to maximize the field in the tumor region. To that end, we first segment the patients' head MRIs into five normal and three abnormal tissues. However, the current method for head segmentation was found to be less accurate at the infratentorial regions, mark some sinuses with cerebrospinal fluid (CSF), and does not identify the cerebellum and brain-stem. Therefore, it is assuming the infratentorial structures have the same electrical properties as in the cerebrum. Methods: We have developed a new method for segmentation of the head MRI that overcomes these limitations. As in the previous method, we have incorporated an atlas that is composed of an MRI image and tissue probability maps (TPM). The TPM assign each voxel in the atlas MRI with a value in the range of 0-1. This value reflects the probability that the specific tissue resides in that voxel. We have generated new customized TPMs for the cerebellum, brain stem and sinuses. Moreover, we have carefully revised the other TPMs to ensure best results. Last, we have incorporated these TPMs in a new atlas-based segmentation method. We have validated the method on 10 GBM patients T1w +gad head MRIs. Results: The average Dice coefficient between gold-standard and algorithms' segmentation was 68.4% (SD=11.2%) that is similar to the value observed with the current method implemented in the treatment-planning software (67.8%; SD=10.6%). However, the new method presented here extends the current method with the cerebellum (average Dice = 84%) and brainstem (average Dice = 67%). Moreover, the sinuses are marked with air, and erroneous CSF segments in the sinuses were removed. Conclusions: We have presented a novel method for the segmentation of the head that facilitates accurate infratentorial and supratentorial structures' segmentation. Moreover, the method eliminates erroneous CSF segmentation in the sinuses. These improvements pave the way for TTFields planning for infratentorial tumors. Citation Format: Yana Glozman, Reuben R. Shamir, Zeev Bomzon. A method for infratentorial structures segmentation for tumor treating fields treatment planning [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2021; 2021 Apr 10-15 and May 17-21. Philadelphia (PA): AACR; Cancer Res 2021;81(13_Suppl):Abstract nr 3071.

Influence of segmentation accuracy in structural MR head scans on electric field computation for TMS and tES

In several diagnosis and therapy procedures based on electrostimulation effect, the internal physical quantity related to the stimulation is the induced electric field. To estimate the induced electric field in an individual human model, the segmentation of anatomical imaging, such as magnetic resonance image (MRI) scans, of the corresponding body parts into tissues is required. Then, electrical properties associated with different annotated tissues are assigned to the digital model to generate a volume conductor. However, the segmentation of different tissues is a tedious task with several associated challenges specially with tissues appear in limited regions and/or low-contrast in anatomical images. An open question is how segmentation accuracy of different tissues would influence the distribution of the induced electric field. In this study, we applied parametric segmentation of different tissues to exploit the segmentation of available MRI to generate different quality of head models using deep learning neural network architecture, named ForkNet. Then, the induced electric field are compared to assess the effect of model segmentation variations. Computational results indicate that the influence of segmentation error is tissue-dependent. In brain, sensitivity to segmentation accuracy is relatively high in cerebrospinal fluid (CSF), moderate in gray matter (GM) and low in white matter for transcranial magnetic stimulation (TMS) and transcranial electrical stimulation (tES). A CSF segmentation accuracy reduction of 10% in terms of Dice coefficient (DC) lead to decrease up to 4% in normalized induced electric field in both applications. However, a GM segmentation accuracy reduction of 5.6% DC leads to increase of normalized induced electric field up to 6%. Opposite trend of electric field variation was found between CSF and GM for both TMS and tES. The finding obtained here would be useful to quantify potential uncertainty of computational results.

An effective approach for white matter, grey matter, and cerebrospinal fluid segmentation from 3D brain MRI

An effective approach for white matter, grey matter, and cerebrospinal fluid segmentation from 3D brain MRI

An effective approach for white matter, grey matter, and cerebrospinal fluid segmentation from 3D brain MRI

According to the World Alzheimer Report 2019, over 50 million people are living with dementia globally and it will increase to 152 million by 2050. Alzheimer's disease (AD) is a common type of dementia. One of the methods for Alzheimer's disease detection is using medical imaging especially using magnetic resonance imaging (MRI) in the brain concerning the white matter (WM), grey matter (GM), and cerebrospinal fluid regions (CSF). There are many methods based on a 3D CNN model for tissue segmentation but the disadvantage of the 3D model compared with the 2D model is the number of training objects will be decreased and the computation cost is increased. In this research, we proposed an effective approach to segment automatically WM, GM, and CSF in 3D brain MRI by using 2D and 3D CNN model combination. Our proposed method was evaluated with IBSR 18 and compared against state-of-the-art methods. The accuracy is 0.93, 0.92 and 0.79 for WM, GM, and CSF segmentation.

An automatic self‐initialized clustering method for brain tissue segmentation and pathology detection from magnetic resonance human head scans with graphics processing unit machine

AbstractThe proposed work introduces a fully automatic modified fuzzy c‐means (MFCM) algorithm for segmenting brain tissue into gray matter (GM), white matter (WM), and cerebrospinal fluid (CSF) which identifies the pathological conditions of magnetic resonance human head scans. The present work implements histogram smoothing using Gaussian distribution for finding the number of clusters (K) and cluster centers (C) to initialize modified FCM algorithm (MFCM). The modification includes the local impact of each pixel based on the median of local neighborhoods. This needs more computational power to reduce the processing time and requires a parallel programming environment like the Graphics Processing Unit. The parallel MFCM is performed with the help of compute unified device architecture language and reduced the processing time up to 80 speedup folds than the serial implementation in Matlab and 20 speedup folds than C programming implementation. The method is examined with the Internet Brain Segmentation Repository (IBSR20) T1W dataset. The quantitative and qualitative results of the proposed method are compared with state‐of‐the‐art‐methods using the Dice coefficient (DC). Proposed method yields high DC 0.84 ± 0.03 for GM, 0.83 ± 0.04 for WM, and 0.41 ± 0.12 for CSF segmentation. In post‐processing, 3D volumes of segmented regions have been constructed and compared with the gold standard quantitatively and qualitatively.

Technical Note: Simultaneous segmentation and relaxometry for MRI through multitask learning.

This study demonstrated a magnetic resonance (MR) signal multitask learning method for three-dimensional (3D) simultaneous segmentation and relaxometry of human brain tissues. A 3D inversion-prepared balanced steady-state free precession sequence was used for acquiring in vivo multicontrast brain images. The deep neural network contained three residual blocks, and each block had 8 fully connected layers with sigmoid activation, layer norm, and 256 neurons in each layer. Online-synthesized MR signal evolutions and labels were used to train the neural network batch-by-batch. Empirically defined ranges of T1 and T2 values for the normal gray matter, white matter, and cerebrospinal fluid (CSF) were used as the prior knowledge. MRI brain experiments were performed on three healthy volunteers. The mean and standard deviation for the T1 and T2 values in vivo were reported and compared to literature values. Additional animal (N=6) and prostate patient (N=1) experiments were performed to compare the estimated T1 and T2 values with those from gold standard methods and to demonstrate clinical applications of the proposed method. In animal validation experiment, the differences/errors (mean difference±standard deviation of difference) between the T1 and T2 values estimated from the proposed method and the ground truth were 113±486 and 154±512ms for T1, and 5±33 and 7±41ms for T2, respectively. In healthy volunteer experiments (N=3), whole brain segmentation and relaxometry were finished within~5s. The estimated apparent T1 and T2 maps were in accordance with known brain anatomy, and not affected by coil sensitivity variation. Gray matter, white matter, and CSF were successfully segmented. The deep neural network can also generate synthetic T1- and T2-weighted images. The proposed multitask learning method can directly generate brain apparent T1 and T2 maps, as well as synthetic T1- and T2-weighted images, in conjunction with segmentation of gray matter, white matter, and CSF.

Infant Brain Structural MRI Analysis in the Context of Thoracic Non-cardiac Surgery and Critical Care.

Objective: To determine brain magnetic resonance imaging (MRI) measures of cerebrospinal fluid (CSF) and whole brain volume of full-term and premature infants following surgical treatment for thoracic non-cardiac congenital anomalies requiring critical care.Methods: Full-term (n = 13) and pre-term (n = 13) patients with long-gap esophageal atresia, and full-term naïve controls (n = 19) < 1 year corrected age, underwent non-sedated brain MRI following completion of thoracic non-cardiac surgery and critical care treatment. Qualitative MRI findings were reviewed and reported by a pediatric neuroradiologist and neurologist. Several linear brain metrics were measured using structural T1-weighted images, while T2-weighted images were required for segmentation of total CSF and whole brain tissue using the Morphologically Adaptive Neonatal Tissue Segmentation (MANTiS) tool. Group differences in absolute (mm, cm3) and normalized (%) data were analyzed using a univariate general linear model with age at scan as a covariate. Mean normalized values were assessed using one-way ANOVA.Results: Qualitative brain findings suggest brain atrophy in both full-term and pre-term patients. Both linear and volumetric MRI analyses confirmed significantly greater total CSF and extra-axial space, and decreased whole brain size in both full-term and pre-term patients compared to naïve controls. Although linear analysis suggests greater ventricular volumes in all patients, volumetric analysis showed that normalized ventricular volumes were higher only in premature patients compared to controls.Discussion: Linear brain metrics paralleled volumetric MRI analysis of total CSF and extra-axial space, but not ventricular size. Full-term infants appear to demonstrate similar brain vulnerability in the context of life-saving thoracic non-cardiac surgery requiring critical care as premature infants.

Automated Ventricular System Segmentation in Paediatric Patients Treated for Hydrocephalus Using Deep Learning Methods.

Hydrocephalus is a common neurological condition that can have traumatic ramifications and can be lethal without treatment. Nowadays, during therapy radiologists have to spend a vast amount of time assessing the volume of cerebrospinal fluid (CSF) by manual segmentation on Computed Tomography (CT) images. Further, some of the segmentations are prone to radiologist bias and high intraobserver variability. To improve this, researchers are exploring methods to automate the process, which would enable faster and more unbiased results. In this study, we propose the application of U-Net convolutional neural network in order to automatically segment CT brain scans for location of CSF. U-Net is a neural network that has proven to be successful for various interdisciplinary segmentation tasks. We optimised training using state of the art methods, including “1cycle” learning rate policy, transfer learning, generalized dice loss function, mixed float precision, self-attention, and data augmentation. Even though the study was performed using a limited amount of data (80 CT images), our experiment has shown near human-level performance. We managed to achieve a 0.917 mean dice score with 0.0352 standard deviation on cross validation across the training data and a 0.9506 mean dice score on a separate test set. To our knowledge, these results are better than any known method for CSF segmentation in hydrocephalic patients, and thus, it is promising for potential practical applications.

Multi stream 3D hyper-densely connected network for multi modality isointense infant brain MRI segmentation

Automatic accurate segmentation of medical images has significant role in computer-aided diagnosis and disease treatment. The segmentation of cerebrospinal fluid (CSF), gray matter (GM), and white matter (WM) tissues plays an important role in infant brain structure for studying early brain development. However, this task is very challenging due to low contrast between GM and WM in isointense phase (approximately 6-8 months of age). In this study, we develop a hyper-densely connected convolutional neural network (CNN) for segmentation of volumetric infant brain. The proposed model provides dense connection between layers to improve the performance of flow information in the network. It also allows the multiscale contextual information by concatenating the feature maps of early, intermediate, and later layers. This architecture employs MR-T1 and T2 as input, which are processed in two separate independent paths, and then their low, intermediate, and high layer features are fused for final segmentation. An important change relative to earlier densely connected networks is the application of direct layer connections from the same and different paths. In this scenario, each modality is processed in an independent path, and dense connections occur not only between layers within the same path, but also between layers in different paths. Adopting such dense connectivity leads to benefits of deep supervision and improved gradient flow. Furthermore, by combining the feature maps of early, intermediate, and late convolutional layers, our architecture injects multiscale information into the final segmentation. This suggested approach is examined in the MICCAI Grand Challenge iSEG and obtains significant advantages over existing approaches in terms of parameter efficiency and segmentation accuracy on 6-month infant brain MRI segmentation.

RP-Net: A 3D Convolutional Neural Network for Brain Segmentation From Magnetic Resonance Imaging

Quantitative analysis of brain volume is quite significant for the diagnosis of brain diseases. Accurate segmentation of essential brain tissues from 3D medical images is fundamental to quantitative brain analysis. Since manual segmentation is extremely tedious and time-consuming, there is a growing demand for automated segmentation. In this paper, we propose a 3D convolutional neural network including recursive residual blocks and a pyramid pooling module (RP-Net) for segmenting brain from 3D magnetic resonance (MR) images into white matter (WM), gray matter (GM), and cerebrospinal fluid (CSF). RP-Net is an U-Net like the network that consists of a downsampling path and an upsampling path. Each path consists of four stages with a recursive residual block. All layers in RP-Net are implemented in a 3D manner. The pyramid pooling module is applied before a voxel-wise classification layer for obtaining both local and global context information. The RP-Net has been evaluated on WM, GM, and CSF segmentation with CANDI, IBSR18, and IBSR20 dataset. The experiments show that the RP-Net achieved mean dice similarity coefficients of 90.7% on CANDI, 90.49% on IBSR18 and 84.96% on IBSR20. The results demonstrate that our proposed method has achieved a significant improvement in segmentation accuracy compared to other reported approaches.