## 2 Materials and Methods
### U-Net Implementation for Autocontouring Algorithm  
Our overall aim was to achieve \(\geq\)0.9 contouring accuracy from the testing images, measured by [PERSON]'s coefficient (DC; \(\in\)[0, 1], where 1 means complete agreement) [48] between manual contours and autocontours. Since the mean (standard deviation (SD)) of intra- and interobserver variations in experts' manual contouring were previously found as 0.88 (0.04) and 0.87 (0.04), respectively [49], which represent inherent uncertainties in  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Site** & **Patient** & **Gender** & **Age** &
\begin{tabular}{c} **Tumor Area** \\ **(cm2)** \\ \end{tabular} & **Overall Stage** & **Cancer Type** \\ \hline \multirow{8}{*}{Lung} & 36 & F & 73 & 6.4 & II & NSCLC \\  & 37 & M & 65 & 3.8 & IA & NSCLC \\  & 38 & F & 78 & 7.4 & I & Lung cancer \\  & 39 & M & 79 & 3.8 & I & NSCLC \\  & 40 & M & 65 & 5.1 & I & NSCLC \\  & 41 & M & 90 & 3.9 & I & Squamous cell carcinoma \\  & 42 & M & 75 & 3.7 & I & NSCLC \\  & 43 & M & 81 & 1.3 & I & NSCLC \\  & 44 & M & 75 & 3.0 & IIA & NSCLC \\  & 45 & M & 70 & 1.7 & IB & SCLC \\  & 46 & M & 65 & 1.4 & IA & NSCLC \\  & 47 & M & 72 & 4.8 & IVA & NSCLC \\ \hline Mean & & & & 11.6 (8.7) & & \\ (SD) & & & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 1: _Cont._manual contours, a comparable number of 0.9 was set as a goal to determine reasonable autocontouring accuracy.  
As the first part of developing such an autocontouring algorithm, a deep neural network using a U-Net architecture was implemented. U-Net, initially developed for biomedical image segmentation by [PERSON] et al. [31], has been widely applied for segmentation tasks in medical imaging involving various sites, patients, and imaging modalities, for which it showed promising performance [50; 51; 52; 53; 54]. As opposed to classification tasks that require a label as output [55; 56], segmentation tasks require a binary image as output; this makes U-Net a suitable choice due to its ability to localize the extracted features which allows to produce an accurate segmentation map as output. Also, U-Net can learn with a limited amount of training data, and it is faster to train than most other segmentation models due to its use of context-based learning [31; 57].  
U-Net is a convolutional neural network whose architecture consists of a contracting path for capturing context and a symmetric expanding path that enables precise localization (see Figure 4a, which is located under Section 3 for comparison to our modified version of U-Net). The contracting (or down-sampling) part extracts features with 3 \(\times\) 3 convolutions, and the expanding (or up-sampling) part uses up-convolution, decreasing the number of feature maps while increasing their dimensions. A feature map is a collection of extracted features forming a 2D matrix that results from convolving an input image with a filter. For example, a 256 \(\times\) 256 \(\times\) 1 input image convolved with \(n\) number of 3 \(\times\) 3 filters results in a 256 \(\times\) 256 \(\times\) \(n\) block of feature maps (using zero paddings to the input image to maintain the height and width dimensions after convolution), forming a 3D matrix. Feature maps from the expanding part are concatenated with the corresponding feature maps from the contracting part to recover localization information. Finally, a 1 \(\times\) 1 convolution creates a linear projection of the feature maps to generate a binary segmentation map. For each patient case used in this study, the original U-Net architecture was modified by optimizing its hyperparameters.