Skull-stripping method for brain MRI using a 3D level set with a speedup operator

State-of-the-Art Traditional to the Machine- and Deep-Learning-Based Skull Stripping Techniques, Models, and Algorithms

Several neuroimaging processing applications consider skull stripping as a crucial pre-processing step. Due to complex anatomical brain structure and intensity variations in brain magnetic resonance imaging (MRI), an appropriate skull stripping is an important part. The process of skull stripping basically deals with the removal of the skull region for clinical analysis in brain segmentation tasks, and its accuracy and efficiency are quite crucial for diagnostic purposes. It requires more accurate and detailed methods for differentiating brain regions and the skull regions and is considered as a challenging task. This paper is focused on the transition of the conventional to the machine- and deep-learning-based automated skull stripping methods for brain MRI images. It is observed in this study that deep learning approaches have outperformed conventional and machine learning techniques in many ways, but they have their limitations. It also includes the comparative analysis of the current state-of-the-art skull stripping methods, a critical discussion of some challenges, model of quantifying parameters, and future work directions.

Methods on Skull Stripping of MRI Head Scan Images-a Review

The high resolution magnetic resonance (MR) brain images contain some non-brain tissues such as skin, fat, muscle, neck, and eye balls compared to the functional images namely positron emission tomography (PET), single photon emission computed tomography (SPECT), and functional magnetic resonance imaging (fMRI) which usually contain relatively less non-brain tissues. The presence of these non-brain tissues is considered as a major obstacle for automatic brain image segmentation and analysis techniques. Therefore, quantitative morphometric studies of MR brain images often require a preliminary processing to isolate the brain from extra-cranial or non-brain tissues, commonly referred to as skull stripping. This paper describes the available methods on skull stripping and an exploratory review of recent literature on the existing skull stripping methods.

Alpha shape theory for 3D visualization and volumetric measurement of brain tumor progression using magnetic resonance images

Resection of brain tumors is a tricky task in surgery due to its direct influence on the patients' survival rate. Determining the tumor resection extent for its complete information via-à-vis volume and dimensions in pre- and post-operative Magnetic Resonance Images (MRI) requires accurate estimation and comparison. The active contour segmentation technique is used to segment brain tumors on pre-operative MR images using self-developed software. Tumor volume is acquired from its contours via alpha shape theory. The graphical user interface is developed for rendering, visualizing and estimating the volume of a brain tumor. Internet Brain Segmentation Repository dataset (IBSR) is employed to analyze and determine the repeatability and reproducibility of tumor volume. Accuracy of the method is validated by comparing the estimated volume using the proposed method with that of gold-standard. Segmentation by active contour technique is found to be capable of detecting the brain tumor boundaries. Furthermore, the volume description and visualization enable an interactive examination of tumor tissue and its surrounding. Admirable features of our results demonstrate that alpha shape theory in comparison to other existing standard methods is superior for precise volumetric measurement of tumor.

Contour-based brain segmentation method for magnetic resonance imaging human head scans

The high-resolution magnetic resonance brain images often contain some nonbrain tissues (ie, skin, fat, muscle, neck, eye balls, etc) compared with the functional images such as positron emission tomography, single-photon emission computed tomography, and functional magnetic resonance imaging (MRI) scans, which usually contain few nonbrain tissues. Automatic segmentation of brain tissues from MRI scans remains a challenging task due to the variation in shape and size, use of different pulse sequences, overlapping signal intensities and imaging artifacts. This article presents a contour-based automatic brain segmentation method to segment the brain regions from T1-, T2-, and proton density-weighted MRI of human head scans. The proposed method consists of 2 stages. In stage 1, the brain regions in the middle slice is extracted. Many of the existing methods failed to extract brain regions in the lower and upper slices of the brain volume, where the brain appears in more than 1 connected region. To overcome this problem, in the proposed method, a landmark circle is drawn at the center of the extracted brain region of a middle slice and is likely to pass through all the brain regions in the remaining lower and upper slices irrespective of whether the brain is composed of 1 or more connected components. In stage 2, the brain regions in the remaining slices are extracted with reference to the landmark circle obtained in stage 1. The proposed method is robust to the variability of brain anatomy, image orientation, and image type, and it extracts the brain regions accurately in T1-, T2-, and proton density-weighted normal and abnormal brain images. Experimental results by applying the proposed method on 100 volumes of brain images show that the proposed method exhibits best and consistent performance than by the popular existing methods brain extraction tool, brain surface extraction, watershed algorithm, hybrid watershed algorithm, and skull stripping using graph cuts.

Brain tumor surgery with 3-dimensional surface navigation

BACKGROUND

Precise lesion localization is necessary for neurosurgical procedures not only during the operative approach, but also during the preoperative planning phase.

OBJECTIVE

To evaluate the advantages of 3-dimensional (3-D) brain surface visualization over conventional 2-dimensional (2-D) magnetic resonance images for surgical planning and intraoperative guidance in brain tumor surgery.

METHODS

Preoperative 3-D brain surface visualization was performed with neurosurgical planning software in 77 cases (58 gliomas, 7 cavernomas, 6 meningiomas, and 6 metastasis). Direct intraoperative navigation on the 3-D brain surface was additionally performed in the last 20 cases with a neurosurgical navigation system. For brain surface reconstruction, patient-specific anatomy was obtained from MR imaging and brain volume was extracted with skull stripping or watershed algorithms, respectively. Three-dimensional visualization was performed by direct volume rendering in both systems. To assess the value of 3-D brain surface visualization for topographic lesion localization, a multiple-choice test was developed. To assess accuracy and reliability of 3-D brain surface visualization for intraoperative orientation, we topographically correlated superficial vessels and gyral anatomy on 3-D brain models with intraoperative images.

RESULTS

The rate of correct lesion localization with 3-D was significantly higher (P = .001, χ), while being significantly less time consuming (P < .001, χ) compared with 2-D images. Intraoperatively, visual correlation was found between the 3-D images, superficial vessels, and gyral anatomy.

CONCLUSION

The proposed method of 3-D brain surface visualization is fast, clinically reliable for preoperative anatomic lesion localization and patient-specific planning, and, together with navigation, improves intraoperative orientation in brain tumor surgery and is relatively independent of brain shift.

3D preoperative planning in the ER with OsiriX®: when there is no time for neuronavigation

The evaluation of patients in the emergency room department (ER) through more accurate imaging methods such as computed tomography (CT) has revolutionized their assistance in the early 80s. However, despite technical improvements seen during the last decade, surgical planning in the ER has not followed the development of image acquisition methods. The authors present their experience with DICOM image processing as a navigation method in the ER. The authors present 18 patients treated in the Emergency Department of the Hospital das Clínicas of the University of Sao Paulo. All patients were submitted to volumetric CT. We present patients with epidural hematomas, acute/subacute subdural hematomas and contusional hematomas. Using a specific program to analyze images in DICOM format (OsiriX^®), the authors performed the appropriate surgical planning. The use of 3D surgical planning made it possible to perform procedures more accurately and less invasively, enabling better postoperative outcomes. All sorts of neurosurgical emergency pathologies can be treated appropriately with no waste of time. The three-dimensional processing of images in the preoperative evaluation is easy and possible even within the emergency care. It should be used as a tool to reduce the surgical trauma and it may dispense methods of navigation in many cases.

An accurate skull stripping method based on simplex meshes and histogram analysis for magnetic resonance images

Skull stripping methods are designed to eliminate the non-brain tissue in magnetic resonance (MR) brain images. Removal of non-brain tissues is a fundamental step in enabling the processing of brain MR images. The aim of this study is to develop an automatic accurate skull stripping method based on deformable models and histogram analysis. A rough-segmentation step is used to find the optimal starting point for the deformation and is based on thresholds and morphological operators. Thresholds are computed using comparisons with an atlas, and modeling by Gaussians. The deformable model is based on a simplex mesh and its deformation is controlled by the image local gray levels and the information obtained on the gray level modeling of the rough-segmentation. Our Simplex Mesh and Histogram Analysis Skull Stripping (SMHASS) method was tested on the following international databases commonly used in scientific articles: BrainWeb, Internet Brain Segmentation Repository (IBSR), and Segmentation Validation Engine (SVE). A comparison was performed against three of the best skull stripping methods previously published: Brain Extraction Tool (BET), Brain Surface Extractor (BSE), and Hybrid Watershed Algorithm (HWA). Performance was measured using the Jaccard index (J) and Dice coefficient (κ). Our method showed the best performance and differences were statistically significant (p<0.05): J=0.904 and κ=0.950 on BrainWeb; J=0.905 and κ=0.950 on IBSR; J=0.946 and κ=0.972 on SVE.