# Applications of Deep Learning to Neurodevelopment in Pediatric Imaging: Achievements and Challenges
## 3 Results
### Identifying Brain and Tissue Structures  
Identifying brain and tissue structures is of great importance in facilitating studies investigating changes in a specific region of interest. Accurate segmentation of brain tissues and structures lays the foundation for volumetric and morphologic analysis. Volumetric \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Study** & **Year** & **Disorder** & **Population** & **Technique** & **Preprocessing** & **Method** & **Results** \\ \hline
[62] & 2021 & Disruptive Behavior Disorder & ABCD Study: 550 patients, 550 HC (age 9–11 years) & sMRI, rs-fMRI, DTI & FSL & 3D CNN & Accuracy 72\% \\ \hline
[58] & 2020 & Dyslexic & 36 patients, 19 HC (age 9–12 years) & task fMRI & SPM & 3D CNN & Accuracy 72.73\% \\ \hline
[94] & 2020 & Embryonic Neurodevelopmental Disorders & 114 patients, 113 HC (age 16–39 weeks) & sMRI & — & CNN & Accuracy 87.7\% \\ \hline
[59] & 2020 & Epilepsy & 30 patients, 13 HC & sMRI & BET & CNN & Accuracy 66–73\% \\ \hline
[60] & 2020 & Epilepsy & 59 patients, 70 HC (age 7–18 years) & DTI & SPM & CNN & Accuracy 90.75\% \\ \hline
[70] & 2021 & Neonatal Hyperbilirubin & 47 patients, 32 HC (age 1–18 days) & sMRI & & CNN & Accuracy 72.15\% \\ \hline
[63] & 2021 & PTSD & 33 patients (age 14.3 \(\pm\) 3.3 years) 53 HC (age 15.0 \(\pm\) 2.3 years) & rs-fMRI & SPM12 & MLP & Accuracy 72\% \\ \hline
[64] & 2020 & Tuber & 260 patients, 260 HC & sMRI & FSL & 3D CNN & Accuracy 97.1\% \\ \hline
[65] & 2022 & Tuber & 296 patients, 245 HC (age 0–8 years) & sMRI & - & 3D CNN & Accuracy 86\% \\ \hline
[71] & 2020 & Tuber & \
\begin{tabular}{c} 114 patients (age 5–15.3 years), 114 HC (age 6.9–15.7 years) \\ \end{tabular} & sMRI &
\begin{tabular}{c} In-house \\ pipeline \\ \end{tabular} & CNN & Accuracy 95\% \\ \hline
[95] & 2021 & Tumor &
\begin{tabular}{c} 136 patients, 22 HC (age 0–11 years) \\ \end{tabular} & sMRI & SPM & CNN & Accuracy 87 \(\pm\) 2\% \\ \hline
[72] & 2020 & Tumor &
\begin{tabular}{c} 617 patients with tumor (age 0.2–34 years) \\ \end{tabular} & sMRI & Pydicom & CNN & Accuracy 72\% \\ \hline
[66] & 2018 & Tumor & 233 subjects & sMRI & - & Capsule & Accuracy 86.56\% \\ \hline
[96] & 2020 & Tumor & 39 pediatric patients & sMRI & - & CNN & Accuracy 87.8\% \\ \hline
[67] & 2020 & White Matter Pathways &
\begin{tabular}{c} 89 patients with focal \\ epilepsy (age 9.95 \(\pm\) 5.41 years) \\ \end{tabular} & DTI & FreeSurfer & CNN & Accuracy 98\% \\ \hline
[68] & 2019 & White Matter Pathways &
\begin{tabular}{c} 70 HC \\ (age 12.01 \(\pm\) 4.80 years), \\ 70 patients with focal \\ epilepsy (age 11.60 \(\pm\) 4.80 years) \\ \end{tabular} & DTI & FreeSurfer, TSR, NIH & CNN & 0.9525 \(\pm\) 0.0053 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: _Cont._analysis of gray matter, white matter, cerebrospinal fluid, and specific brain structure such as amygdala assist in computer-aided diagnosis of neurodevelopmental disorders. Localization and segmentation of brain tumor is essential for assessment of the tumor burden as well as treatment response and tumor progression [97]. Brain masking isolates the brain from surrounding tissues across non-stationary 3D brain volumes in fMRI, which is important and challenging, especially for fetal imaging [98]. Specific challenges for pediatric brain segmentation exist due to the variations in head size and shape in children compared to adults. Rapid changes in tissue contrast and low contrast to noise ratio in fetal and newborn MRIs lead to further demanding techniques [99]. This study reviews segmentation of pediatric brain tissues, structures, tumors, and masking of fetal brain (Table 3). Most of the studies employed U-net for segmentation. Dice scores vary across studies. 3D U-net models were implemented for brain tissue and volume segmentation [25; 100; 101; 102]. Transfer learning and active learning greatly reduced the number of samples that need to be labeled for training a high-quality patch-wise segmentation method [99].