2. Related Works
2.1. Brain Tumor in Childhood
- Gliomas: Is a generic name for a number of cancers, including:
- Astrocytomas (which include glioblastomas): From a specific type of glial cells called astrocytes, these types of tumors are usually started. They are often grouped by grade. Low-grade astrocytomas include pilocytic astrocytomas, subependymal giant cell astrocytomas (SEGAs), diffuse astrocytomas, pleomorphic xanthoastrocytomas (PXAs) and optic gliomas. High-grade astrocytomas include glioblastomas and anaplastic astrocytomas.
- Oligodendrogliomas: From a specific type of cerebral cells called oligodendrocytes, these types of tumor are usually started. Oligodendrogliomas have been categorized as Grade II tumors that account for over 1% of children’s brain tumors.
- Ependymomas: From the ependymal cells which line the spinal cord, begins this type of tumors which responsible for around 5% of brain tumors in children. They can vary from Grade I tumors to Grade III tumors (anaplastic ependymomas).
- Brainstem gliomas: This tumor is a glioma that develops in brain stem and is responsible for around 10% to 20% of brain tumors in children. They are common in two types: focal brain stem gliomas or diffuse midline gliomas.
- Embryonal tumors: These tumors begin in the central nervous system, in early forms of nerve cells. In children, they are common among younger children rather than older children. Embryonic tumors account for around 10–20% of brain tumors, including the most frequent type: medulloblastomas, and less common types such as medulloepithelioma, and atypical teratoid (ATRT).
- Pineal tumors: Are there any types of tumors that could be found in the pineal gland? The most popular, fastest growing and difficult to treat type of these forms is called pineoblastomas.
- Craniopharyngiomas: Craniopharyngiomas account for approximately 4% of children’s brain tumors. They occur over the pituitary gland, but it is under the brain itself that these slow-growing tumors begin.
- Mixed neuronal and glial tumors: This type of tumor combined between neuronal and glial tumors. They include dysembryoplastic neuroepithelial tumors (DNETs) and gangliogliomais.
- Choroid plexus tumors: They are a rare tumor, many of which are benign and some are malignant.
- Schwannomas: They begin in cells that surround and separate the cranial nerves and other nerves. These rare tumors are usually benign.
- In or near brain tumors: These include chordomas, tumors of germ cells, neuroblastomas, pituitary tumors, meningiomas (Grade I to Grade III) and lymphomas.
- Metastatic or secondary brain tumors: The tumors that begin in other organs and then spread to the brain are metastatic or secondary brain tumors. They are often less frequent than primary brain tumors and often treated differently.
2.2. Pediatric Brain Imaging Technique
2.3. Reading MRI Sequences
2.4. Available Pediatric Brain Datasets
- dHCP: The Developing Human Connectome Project (dHCP)  is an ERC-funded collaboration between King’s College London, Imperial College London and the University of Oxford. dBCP has two data releases as to date. The first open access data release consists of images of 40 representative term neonatal subjects. The imaging data includes structural imaging, structural connectivity data (diffusion MRI) and functional connectivity data (resting-state fMRI). The second open access data release consists of images of 558 neonatal subjects. The released dataset includes T1w and T2w structural data supplied as initial image data and after pipeline preprocessing. The images included in this release were obtained from infants born and imaged between 24–45 weeks of age. Using a dedicated neonatal imaging device which included a neonatal 32 channel phased array head coil, imaging was carried out on 3T Philips Achieva.
- PBTA: Pediatric Brain Tumor Atlas (PBTA)  is a collaborative effort, which is led by the Children’s Brain Tumor Tissue Consortium (CBTTC), to accelerate discoveries for therapeutic intervention for children brain tumors diagnosed. The first release of the Pediatric Brain Tumor Atlas (PBTA) dataset, which comprises over 30 different types of pediatric brain tumors covering over 1000 subjects, occurred on September, 2018. Data types include match tumor/normal, whole genome data (WGS), RNAseq, proteomics, longitudinal clinical data, imaging data (including MRIs and radiology reports), histology slide images and pathology reports.
- HCP: The Lifespan Human Connectome Project Development  lunch Lifespan HCP Release 1.0 in May 2019 for HCP-Development and HCP-Aging. All HCP-development (ages 5–21) data is shared in the NIMH Data Archive, NDA Collection. Lifespan HCP Release 1.0 data includes unprocessed data of all modalities (structural MRI, resting state fMRI, task fMRI, and diffusion MRI) for 655 HCP-D subjects, minimally preprocessed structural MRI data (only) for 84 subjects, and basic demographic data (age, sex, race/ethnicity, and handedness) for all released HCP-D subjects.
- PING: Pediatric Imaging, Neurocognition, and Genetics  data of 1400 children aged between 3 and 20 years are included in this genetics data resource. PING data access is thoroughly handled by the NIMH Data Repository.
- iSeg-2017 and iSeg-2019: Challenge data six-month infant brain MRI segmentation (iSeg-2017) . Comparing (semi-)automatic algorithms for the segmentation of 6-month infant brain tissues and the calculation of corresponding structures was its goal of the iSeg-2017 competition. On a Siemens head-only 3 T scanner with a circular polarized head coil, all scans for the 10 infant subjects were obtained. The six-month infant brain MRI segmentation (iSeg-2017)  aims to facilitate automated six-month infant brain MRI segmentation algorithms from multiple sites. They offered iSeg-2017 data for training datasets. For the validation dataset, 13 T1 and T2 subject MR images are given. T1- and T2-weighted MR images from three different sites are used in the test dataset.
- IBSR: Internet Brain Segmentation Repository . Along with magnetic resonance brain image data, IBSR provides manually-guided expert segmentation results. Its aim is to promote the assessment and development of methods of segmentation. This dataset contains eighteen currently available subjects aged 7–71 years.
- ABIDE I and ABIDE II: The first ABIDE  project launched in August 2012 reflects Autism Brain Imaging Data Sharing (ABIDE I). Seventeen foreign sites were interested in ABIDE I, exchanging previously acquired resting state functional magnetic resonance imaging (R-fMRI) data. ABIDE I is comprised of 1112 datasets, including 539 from ASD individuals and 573 from typical controls. ABIDE II . In order to further encourage research on the brain connectome in ASD, ABIDE II was released in 2016. There are 19 sites in ABIDEII, donating a total of 1114 datasets from 521 ASD individuals and 593 typical controls.
- CoRR: The Consortium for Reliability and Reproducibility . The goal was to create an open science database for the imaging community to facilitate the assessment of the reliability and reproducibility of functional and structural connectomics studies. CoRR contains 33 datasets, 32 of which are available for download at present. Four of these datasets contains pediatric brain MIR images. IPCAS 2 includes 35 typically developing children. Each participant underwent two scanning sessions one month apart. Three modalities (T1/EPI (echo planar imaging)/DTI (diffusion tensor imaging)) of brain images were acquired for all subjects. IPCAS 7 includes 74 typically developing children. Each participant was scanned twice within a session. Three modalities (T1/T2/EPI) of brain images were acquired for all subjects.
2.5. Data Acquisition and Analysis Methods for Human Brain Activity
3. Pediatric Brain Tumor Deep Learning-Based Studies
3.1. Pediatric Brain Tumor Detection and Classification
3.2. Pediatric Brain Tumor Segmentation
3.3. Related Pediatric Brain Tumor Studies
Nøhr, et al. (2018) 
|Air, soft tissue and|
|79 children (aged|
between 2–14 years)
0.74/0.79 in soft tissue,
0.53/0.70 in bone tissue,
0.57/0.62 in air
|Wang, Geliang, |
et al. (2020) 
|Brain region volume|
Properties of brain
|BET, iBEAT and|
iBEAT with manual
|3D T1WI||22 neonates (13|
boys and 9 girls)
|Brain regions analysis:|
significant differences in 50
brain region with
iBEAT with manual
correction showed the more
accurate brain segmentation
|Chang, Alex, et al.|
+ FID/DFD VAE for
|360 wbMRI slices||90 healthy patients|
(ages 4 to 18)
|FID, DFD, false positive|
(457.30, 23.72, 0%) for
(481.3, 19.378, 0%) for
(442.61, 18.56, 20%) for
(497.09, 17.234, 30%) for
4. Medical and Technical Challenges
5. Conclusions and Future Directions
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
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|Arle, Jeffrey E., et al. (1997) ||Posterior fossa |
|MRS + MR +|
(33 children 6
|Classification accuracy rate|
|Bidiwala, S. and|
|Neural networks||CT + MRI|
(T1WI, T2WI) +
|Classification accuracy rate|
|Quon, J.L., et al.|
study (617 children)
|Detection accuracy was|
AUROC of 0.99
Classification accuracy was
|Ye, Zezhong, et al. (2020) ||Several histologic|
elements of tumors
|DHI model (DBSI +|
|9 pediatric brain|
|Prince, Eric W., et al. (2020)|
|CNN + genetic|
algorithm as a
|CT + MRI +|
combined CT and
study (39 children)
85.3%, 83.3%, and 87.8%,
in respect to modality.
|Zhang, Wenlu, et al.|
|Segmenting all three|
types of brain
|Four 2D CNN||T1, T2, fractional|
(10 infants, 6–8
months of age)
|Overall dice ratios|
|Cui, Zhipeng, et al.|
small sets 4–5 MRI
from each subject (6
to 17 year old age group)
|Accuracy rate of 90%|
|Moeskops, Pim, et al. (2016) ||8 subjects: CB,|
mWM, BGT, vCSF,
uWM, BS, cGM,
(10 images at 30
weeks, 12 images
at 40 weeks, 15
images at 23 years, 20
images at 70 years)
|Average dice ratios|
0.87 (coronal T2w 30
weeks), 0.82 (coronal T2w
40 weeks), 0.84 (axial
T2w 40 weeks), 0.86 (axial
T1w 70 years) and 0.91
(sagittal T1w 23 years).
|Nie, Dong, et al.|
|Segmenting all three|
types of brain
|T1, T2, fractional|
10 healthy infants
(6–8 months of age)
|Average dice ratios|
FCNs (0.838 for CSF
0.861 for GM
0.885 for WM)
mFCNs (0.855 for CSF
0.873 for GM
0.887 for WM)
|Rajchl, Martin, et al.|
|CNNs + fully|
|Public dataset |
(55 fetal MRI
CNNnaïve (74.0), DCBB
(86.6), DCPS (90.3),
|Xu, Yongchao, et al.|
BGT, UWM, BS,
CB, Vent, CSF)
adults (CSF, WM,
|FCN + TL (VGG 16|
|T1, T1-IR, FLAIR|
CoGM (0.79–0.87), BGT
(0.91–0.95), BS (0.76–0.86),
CB (0.91–0.94), Vent
GM (85.40), WM (88.98),
|Zeng, Guodong, and|
infant brain MRI
(CSF, GM, WM)
|3D FCNNs||T1 and T2 weighted|
|Dice overlap coefficient|
|Nie, Dong, et al.|
infant brain MRI
(CSF, GM, WM)
|3D FCNNs||T1, T2, fractional|
(11 healthy infants
0.9190 for WM,
0.9401 for GM,
0.9610 for CSF
|Khalili, Nadieh, et al. (2019) ||Segment of seven brain tissue classes: cerebellum, basal ganglia and thalami,|
ventricular cerebrospinal fluid, white matter, brain stem, cortical gray matter
and extracerebral cerebrospinal fluid.
|2D FCN with identical U-net architecture||T2-weighted MRI||Self-acquired|
12 fetuses (22.9–34.6 weeks
post menstrual age) + neonatal MRI (40 weeks of post menstrual age) From the NeoBrainS12 dataset
|DC over all tissue classes increases to 0.88|
and MSD decrease to 0.37 mm
|Dolz, Jose, et al.|
|Segmenting all three|
types of brain
|3D FCNNs||Integrated T1 and|
(CSF 0.9580, WM 0.9183
and GM 0.9035)
|Dolz, Jose, et al.|
infant brain MRI
(CSF, GM, WM)
|3D FCNNs||T1-weighted and|
|Accuracy rate 92–96%|
Ranked as first or second
in most metrics in the
et al. (2020) 
|SLANT + TL||T1-weighted brain|
MRI with and
|Public dataset—Open Access Series|
on Imaging Studies
(OASIS) 45 subjects
aged 18–96 years old, 30 pediatric subjects
36 subjects paired
Pediatric: 0.89 Contrast:
|Ding, Yang, et al.|
|Three types of brain|
Project), 40 healthy
accuracy rate: 88–90%
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