The term skull stripping refers to the process of eliminating the skull for analysis of anomalies for the brain to get identified through the MRI scans. MRI stands for the Magnetic Resonance Image. The process involves the segmentation of the tissues from the non-brain tissues. “In this article, we are presenting the interesting and essential facts indulged in the skull stripping deep learning with crucial edge covering”. Now, let’s we get started with the basic concepts of skull stripping for your better understanding.
What is Skull Stripped?
- Brain anomalies are identified by the skull stripping
- Brain tissues are segregated from the non-tissues
- The skull stripping process is qualified by the non-tissue amputation
As we are already stated in the small introduction of the skull stripping here we have listed you significance of skull stripping again for ease of understanding. Here, you may get an interrogation on why do we need to have the skull stripping. Our technical team wanted to clarify your doubts from the initial stage itself. This article is fully contented with the aspects based on skull stripping deep learning. Come let us try to understand the need for skull stripping.
Why Skull Stripping?
- Tissue Segmentation
- Fast & Exact Results
In short, skull stripping is used in the medical field to analyze brain abnormalities within a fraction of seconds. It is modeled to segment the non-tissues of the brain from the MRI scans. Its fast process and exact results are the core components that play the dominant role in the analysis of the brain.
In fact, the combination of skull stripping deep learning is astounding the medical world. We cannot imagine the MRI brain image analysis without the skull stripping mechanism. Neurons in the brain can be captured by the 2 modalities. Our researchers of the skull stripping system have itemized the different image modalities for skull stripping to make you understand.
Different Image Modalities for Skull Stripping
- CT- Computed Tomography
- MRI- Magnetic Resonance Imaging
The listed above are the 2 important image modalities used in the skull stripping. CT and MRI have their pros and cons. MRI handles the hard tissues of the brain. Besides MRI scans are limited to the accuracy and the fontanels locations absenteeism whereas CT scans handle the soft tissues of the brain. On the other hand, CT scans are limited to effective contrasts, radiations & tissue representations. Though, CT scans are highly chosen for cranial studies. MRI and CT scans are also concerned with the following.
- Improved Xenon CT
- SPECT & PET-CT
- LDCT & CT
- MRI Diffusion
- Longitudinal & Functional MRI
- Synthetic MRI
As of now, we have discussed the basic concepts of skull stripping. The above listed are the different modalities of skull stripping. Our researchers are very familiar with these modalities and other important deep learning modalities. In the event of habitually researching the skull stripping deep learning concepts, they are very particular about each and every edge of the same field. In the upcoming passage, our researchers have pointed out to you the challenges concreted in the skull stripping for the ease of your understanding.
What are the Challenges of Skull Stripping?
- Challenge 1:
- Voxel Absence
- Challenge 2:
- White Histogram
- Challenge 3:
- Gray Histogram
- Challenge 4:
- Cerebrospinal Dura & Fluid
- Scan Noises
The above aforementioned are some of the challenges paired with the skull stripping. Gray histograms result in the central peak and have the least voxels in the tissue values whereas, white histograms result in the right side peaks. Noises in the scans and cerebrospinal aspects may result in the histogram peaks.
Commonly, deep learning is one of the growing technologies which has a wide range of scope in recent days. Doing skull stripping with deep learning-based projects will yield you the best experience. It also ensures you grab the best career opportunities. Usually, it needs an expert’s assistance to have better persecutions in the crucial edges. In our organization, we have experts who are well versed in all the fields of technology. Furthermore, we will discuss the deep learning approaches for skull stripping.
What is the Deep Learning Approaches for Skull Stripping?
- Central Pixel Segmentation (Voxel CNN)
- Entire Image Segmentation (FCN)
Central pixel segmentation gets done voxel-wise by the CNN (Convolutional Neural Network) layers. The complete image is segmented by the FCN (Fully Convolutional Networks). Deep learning concepts are pampered with the neural networks and they process with the neural network layers. When it comes to the skull stripping the deep learning concepts MRI scans are processed under the numerous layers of the neural network. The above listed are the 2 eminent approaches handpicked in deep learning.
In the earlier statements, we have pointed you to the different image modalities for the skull stripping. As this article is concerned with the skull stripping deep learning, we wanted to list out the deep learning methods and modalities for skull stripping for effective understanding. Shall we get into that phase? Come let us try to understand them.
Deep Learning Methods and Modality for Skull Stripping
- Hybrid Skull Stripping Method
- Dataset
- OASIS
- IBSR
- LPBA40
- MR Modality
- Weighted T1/T2 Images
- Dataset
- Complementary Segmentation Network Method
- Dataset
- OASIS
- MR Modality
- Weighted T1 Images
- Dataset
- PSSNet & DSN Method
- Dataset
- Private
- OASIS
- IBSR
- LPBA40
- MR Modality
- Weighted T1 Images
- Dataset
- 3D U-Net & Novel CNN Method
- Dataset
- NFBS
- OASIS
- MR Modality
- Weighted T1 Images
- Dataset
- MCNN Method
- Dataset
- NAMIC
- Brain Web
- MR Modality
- Weighted T1/T2 Images
- Dataset
- ASM CNN Method
- Dataset
- OASIS
- IBSR
- LPBA40
- MR Modality
- Weighted T1 Images
- Dataset
- Mask RCNN Method
- Dataset
- OASIS
- CC 359
- LPBA40
- MR Modality
- Weighted T1 Images
- Dataset
- Augmentation based Mask Method
- Dataset
- OASIS
- LPBA40
- CC 359
- MR Modality
- Weighted T1 Images
- Dataset
- VoxResNet Method
- Dataset
- Private
- MR Modality
- FLAIR & Weighted T1/T2 Images
- Dataset
- MONSTR, AutoNet& 3D CNN Method
- Dataset
- NAMIC
- MRBrainS
- ADNI
- OASIS & LPB40
- IBSR
- MR Modality
- FLAIR & Weighted T1/T2 Images
- Dataset
In fact, our researchers are familiar with these methods. By doing, so many experiments in the skull stripping under various algorithms, our researchers deliberately know that every aspect consisted in the skull stripping deep learning.
Now, you might get questions on what are all the algorithms used in the skull stripping. Usually, they are huge in numbers, here we are going to state to you the eminent algorithms of the skull stripping for your understanding.
Latest Skull Stripping Deep Learning Algorithms
- SegNet & Yolo
- SQMV & LVQ
- CompNets & PSSNet
- DSN & CNN
- ASM & 3D CNN
- MCN & AutoNet
- VoxResNet
- MONSTR
- Kernel PCA
- SMOTE
The listed above are the important algorithms that are used in skull stripping. We hope that you are getting points as above mentioned. Apart from this datasets of the skull stripping are playing a vital role in the same system.
In the subsequent passage, our researchers completely explained to you the 8 datasets. Our technical team is well versed in these datasets. Usually, we offer the projects according to the upcoming datasets. We know that you are getting excitements about the dataset explanations. Let us have quick insights.
Datasets for Skull Stripping
- CC359
- Consists of adult age groups from 29-80 with 359 datasets
- Segmented results for each gender
- CC 12 (Calgary Campinas) with magnetic strength of 1.50 to 3.00 T from general electric/Philips
- Voxel size 1*1*1 (mm3) & segmented gender dataset (6+6)
- Dual segmentation of volumes (12)
- NFBS
- NFBS- Neuro Feedback Skull Stripped
- Consists of adult age groups from 21-45 with 125 MR brain images
- Brain images (48:men and 77:women)
- Size of the matrix 256*256*192
- Size of the voxel 1*1*1 (mm3)
- NAMIC
- NAMIC- National Alliance for Medical Image Computing
- Consists of 20 T2W skull data
- Improved by Kitware
- MRBrainS13
- Consists of adult age groups from 65 to 80 with 20 MR brain/stroke images
- Automated lesions & atrophy degrees algorithm
- MRI scans with 3.00 T
- IBSR
- IBSR- Internet Brain Segmentation Repository
- Consists of age groups from 7-71 with 18 MR brain images
- Size of the voxel 1.5 (mm) with volumetric (T1W) image
- 43 labeled manually segmented results (Talairach)
- LPBA40
- Consists of 40 volunteers 56 MRI scans
- Digital structure of atlases (brain)
- Protocol labeling & pair evaluation of 6/40 brains
- 34 brains status after the optimum consistency
- OASIS
- OASIS1
- Consists of age groups from 18-80 with 416 subjects
- Alzheimer’s disease diagnosis (60 years / 100 subjects)
- Dementia (20 subjects)
- OASIS2
- Consists of age groups from 60-96 with 150 subjects
- 373 MRI scans, 14 dementia diagnosis & 64 non-dementia diagnosis
- OASIS3
- Consists of surveyed MRIs above 1000
- 609- normal cognitive and 489- declined cognitive of 42-92 age group
- Sessions of MRI is 2000 and sequences of T1W/T2W, diffusion tensor/ susceptibility weighted imaging & FLAIR
- Free surfer segmentation
- OASIS1
- ADNI
- ADNI1 stage T1W/T2W (1.50 T) sequences of dual-echo
- ADNI2 stage T1W/T2W (3.00 T) sequences of dual-echo
- ADNI3 stage (3.00 T) sequences of dual-echo
- Improved scanners Siemens, Philips & General Electric
The above listed are the various datasets used in skull stripping very commonly. In fact, our technical team is very particular about these datasets. Since they are having incredible skull sets which stand out from others. They do research and projects according to the trends in the technology industry. You can also master the technical areas with our researcher’s guidance.
Next, we have given you the list of performance measures for the skull stripping for the ease of your understanding in the same field. It is very important to consider their effective progression according to the performance measures. Come let us try to understand them.
Skull Stripping Performance Analysis
- Erroneous Boundary Voxels %
- Volume Error Rate
- Visual Inspection Ratio
- CI- Containment Index
- ICCs- Intraclass Correlation Coefficients
- LOOV- Leave-One-Out Cross-Validation
- FNR- False Negative Rate
- FPR- False Positive Rate
- SDD- Surface Distance Deviation
- MSSD- Maximum Symmetric Surface Distance
- ASSD- Average Symmetric Surface Distance
- HD- Hausdorff Distance
- Specificity, Sensitivity, Dice, and Jaccard
So far, we have discussed all the possible aspects comprised in the skull stripping deep learning projects. If you still need any clarifications on the above-mentioned and other areas you are always welcome. We are always there for you to assist you. Your success in the determined projects is our main objective. Let’s hold our hands to build your bright future.
