# MR Images, Brain Lesions, and Deep Learning
## 2 The Literature Review: Selection Criteria
### Computer-Aided Diagnosis in Medical Imaging (CADx System)
#### 3.2.3 Image Segmentation  
In simple terms, image segmentation is the procedure of separating a digital image into a different set of pixels [37] and is considered the most fundamental process as it extracts the region of interest (ROI) through a semiautomatic or automatic process [176]. It divides the image into areas according to a specific description to obtain the anatomical structures and patterns of diseases. [PERSON] et al. [166] and [PERSON] and [PERSON] [37] indicate that the principal goal of medical image segmentation is to make things simpler and transform it \"into a set of semantically meaningful, homogeneous, and nonoverlapping regions of similar attributes such as intensity, depth, color, or texture\" [166] because the segmentation assists doctors to diagnose and make decisions [37]. According to [PERSON] et al. [166], the segmentation methods for brain MRI are classified into (i) manual segmentation, (ii) intensity-based methods (including thresholding, region growing, classification, and clustering), (iii) atlas-based methods, (iv) surface-based methods (including active contours and surfaces, and multiphase active contours), and (v) hybrid segmentation methods [166]. To evaluate, validate, and measure the performance of every automated lesion segmentation methodology compared to expert segmentation [177], one needs to consider the accuracy (evaluation measurements) and reproducibility of the model [178]. The evaluation measurements compare the output of segmentation algorithms with the ground truth on either a pixel-wise or a volume-wise basis [5]. The accuracy is related to the grade of closeness of the estimated measure to the true measure [178], and for that, four situations are possible: true positives (TPs) and true negatives (TNs), where the segmentation is correct, and false positives (FPs) and false negatives (FNs), where there is disagreement between the two segmentations. The most commonly used metrics to evaluate the automatic segmentation accuracy, quality, and strength of the model are [179]:  
* _Dice similarity coefficient (DSC)_: Gives a measure of overlap between two segmentations (computed and corresponding reference) and is sensitive to the lesion size. A DSC of 0 indicates no overlap, and a DSC of 1 indicates a perfect overlap; 0.7 normally is considered good segmentation [38; 178; 179; 180; 181]. \[DSC=\frac{2 TP}{FP+FN+2 TP}\] (8) \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Dataset** & **Details** & **Type: Private** & **Website** & **Reference/ Support** \\
**Name** & **Details** & **Public** & **Website** & **Support** \\ \hline \hline \end{tabular} \begin{tabular}{c c} \hline \hline \multicolumn{1}{c}{\
\begin{tabular}{c} **Dataset** \\ **Name** \\ \end{tabular} } &
\begin{tabular}{c} **Details** \\ **Details** \\ **Public** \\ \end{tabular} & **Website** & **Reference/ Support** \\ \hline \hline \end{tabular}
\end{table} 
Table 4: _Cont._* _Precision:_ Is the measure of over-segmentation between 0 and 1, and it means the proportion of the computed segmentation that overlaps with the reference segmentation [179; 180]. This also is called the positive predictive value (PPV), with a high PPV indicating that a patient identified with a lesion does actually have the lesion [182]. \[Precision=\frac{TP}{FP+TP}\] (9)
* _Recall, also known as sensitivity:_ Gives a metric between 0 and 1. It is a sign of over-segmentation, and it is a measure of the amount of the reference segmentation that overlaps with the computed segmentation [179; 180]. \[Recall=Sensitivity=\frac{TN}{TN+FN}\] (10)  
The metrics of overlap measures that are less often used the sensitivity, specificity (measures the portion of negative voxels in the ground-truth segmentation [183]), and accuracy, which, according to [PERSON] et al. [178] and [PERSON] [183], should be considered carefully because these measures penalize errors in small segments more than in large segments.