## 3 Results
### Segmentation Tasks
#### 3.1.6 Machine Learning
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Task**} & \multirow{2}{*}{**Method**} & **First** & **Database** & **Description** & **Results** \\ \cline{3-5}  & & **Author (Year)** & **223D0** & **Description** & **Results** \\ \hline \multirow{6}{*}{**Task**} & \multirow{3}{*}{**Thresholding**} & \multirow{3}{*}{**Alam 2017 [28]**} & 36 SCR, 26 healthy & \multirow{3}{*}{**Global thresholding**, morphological functions, and fractal dimension analysis.} & No segmentation validation, \\  & & 20 & 3 \(\times\) 3 mm\({}^{2}\) & &
\begin{tabular}{c} No segmentation validation, \\ FZ-contour irregularity was more sensitive to SCR presence then FAZ area. \\ \end{tabular} \\ \cline{2-5}  & \multirow{3}{*}{**[PERSON] 2019 [22]**} & 123 DR, 108 healthy & Multi-scale line detector, **Ossu thresholding** for large vessel segmentation. Frang Hessian filter and **global thresholding for all DSC = 0.90 \\  & & 6 \(\times\) 6 mm\({}^{2}\) & vessels segmentation, skeletonization. \\ \cline{2-5}  & \multirow{3}{*}{**Edge** detector} & \multirow{3}{*}{**[PERSON] 2019 [75]**} & 213 subjects & Morphological operators, white top-hat operator, **Canny edge detector**, morphological closing, inversion, removal of small \\  & & 3 \(\times\) 3 mm\({}^{2}\) & 6 \(\times\) 6 \(\times\) 6 mm\({}^{2}\) & objects. \\ \cline{2-5}  & \multirow{3}{*}{**Active Contour Models**} & \multirow{3}{*}{**[PERSON] 2018 [73]**} & 66 DR, 19 healthy & \multirow{3}{*}{**GCGIVE snake model**.} & Jaccard = 0.87 \(\pm\) 0.06 (healthy) \\  & & 20 & 0.86 \(\pm\) 0.09 (diabetes with DR) & 0.89 \(\pm\) 0.09 (diabetes with DR) \\  & & 3 \(\times\) 3 mm\({}^{2}\) & & 0.89 \(\pm\) 0.09 (supervised NPR) \\  & \multirow{3}{*}{**Active Contour Models**} & \multirow{3}{*}{**[PERSON] 2018 [70]**} & 82 mild DR, 23 healthy & \multirow{3}{*}{**GCGIVE model** for contrast improvement, joint **Markov Gibbs model** to segment, **IOMSGRY** model to overcome low contrast, segmentation refinement with 2D connectivity filter.} & DSC = 0.93 \(\pm\) 0.06 \\  & & 6 \(\times\) 6 mm\({}^{2}\) & segmentation refinement with 2D connectivity filter. & \multirow{3}{*}{**DSC = 0.9243**} \\ \cline{2-5}  & \multirow{3}{*}{**[PERSON] 2020 [72]**} & 20 training / 37 test & & \\  & & 20 & **Level Set model** (ImageJ). & \multirow{3}{*}{**DSC = 0.9760**} \\  & & 3 \(\times\) 3 mm\({}^{2}\) & & \\ \hline \multirow{6}{*}{**Deep learning**} & \multirow{3}{*}{**Guo 2019 [60]**} & 405 images & \multirow{3}{*}{**UNet**, thresholding and largest connected region extraction and hole filling.} & \multirow{3}{*}{**DSC = 0.9760**} \\  & & 3 \(\times\) 3 mm\({}^{2}\) & & \\ \cline{1-1} \cline{2-5}  & \multirow{3}{*}{**Lai 2020 [54]**} & 316 volumes & & \\ \cline{1-1} \cline{2-5}  & & 310 D 20 & & \\ \cline{1-1} \cline{2-5}  & & 6 \(\times\) 6 \(\times\) 2 mm\({}^{2}\) & & \\ \cline{1-1} \cline{2-5}  & \multirow{3}{*}{**[PERSON] 2021 [57]**} & 80 subjects & \multirow{3}{*}{**Normalization**, **custom made network**: boundary alignment module (SAM) implemented to extract global information.} & \multirow{3}{*}{**DSC = 0.88**} \\ \cline{1-1} \cline{2-5}  & & 3 \(\times\) 3 mm\({}^{2}\) & & \\ \cline{1-1} \cline{2-5}  & \multirow{3}{*}{**Li preprint [55]**} & 50 images & \multirow{3}{*}{**IFN-V2**: addition of plane perceptron to enhance the perceptron ability in the horizontal direction + global retraining. 3D volume to 3d DSC = 0.9755 \\ \cline{1-1} \cline{2-5}  & & 3\(\times\) 3 mm\({}^{2}\) & & \\ \cline{1-1} \cline{2-5}  & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: _Cont._  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline