## 3 Results
\begin{table}
\begin{tabular}{l l} \hline
**Paper** & **Description** \\ \hline [PERSON] et al. (2018) [20] & ReSU-Net with recurrent connections to exploit temporal information. \\ [PERSON] et al. (2017) [21] & ReSU-Net with boundary attended loss function. \\ [PERSON] et al. (2022) [22] & The five-specific U-Nets are trained to produce view-specific segmentation, which are the input of a fusion network that produces a confidence map on which the final segmentation is created. \\ [PERSON] et al. (2022) [23] & In a traditional U-Net the loss function is evaluated at different stages of the network with the aid of a prostate boundary map. \\ [PERSON] et al. (2014) & A multi-stage network that enhances the feature space to improve segmentation results. A traditional U-Net is modified by replacing the encoding branch with a pre-trained VGG16 model. The output feature maps are then the input of a similar second network. This process is repeated three times and the final segmentation is conducted on the refined feature map and the segmentations that were created after each network stage. \\ [PERSON] et al. (2018) [25] & Integration of spatial information in standard U-Net by taking 2-6 adjacent slices as training input. \\ [PERSON] et al. (2018) [26] & Integration of spatial information in standard U-Net by taking 2-6 adjacent slices as training input. \\ [PERSON] et al. (2020) [27] & A prostate shape mask reconstruction is generated based on prostate boundary coordinates that are predicted from a U-Net back-bone layer. Then, the final segmentation is obtained by merging the shape mask reconstruction with the output of the U-Net decoder. \\ [PERSON] et al. (2019) [16] & The final segmentation is created based on the average of 5 U-Nets and adjusted with the aid of an MRI-based statistical shape model. \\ [PERSON] et al. (2021) [28] & Modified U-Net that learns based on ROI-specific feature maps. \\ [PERSON] et al. (2019) [29] & There view-specific V-Nets that estimate prostate volume with the aid of Multi Directional Contour refinement. The view-specific V-Nets are optimized with the aid of deep supervision. \\ [PERSON] et al. (2022) [30] & A 3D real-net with channel attention implemented. \\ [PERSON] et al. (2023) [31] & Modified U-Net in which spatial attention and a detail compensation module are integrated. \\ [PERSON] et al. (2020) [32] & Modified U-Net to predict radial slices that are utilized for 3D volume reconstruction. \\ [PERSON] et al. (2022) [34] & Modified U-Net that learns based on ROI-specific feature maps. \\ [PERSON] et al. (2024) [34] & Architecture that inherits the ability of deep learning models to locate the ROI with the incorporation of mathematical models to smooth the contour of ROI. \\ [PERSON] et al. (2021) [35] & U-Net trained by 5D3 to improve generalization. \\ [PERSON] et al. (2022) [16] & Coordination Dilated-ReU-Net that was trained by SDa to improve generalization. \\ [PERSON] et al. (2019) [37] & Modified U-Net in which spatial attention is integrated. \\ [PERSON] et al. (2020) [38] & Traditional U-Net. \\ [PERSON] et al. (2022) [39] & Modified U-Net that is trained by shadow-enhanced images and a modified feature space. \\ \hline \end{tabular}
\end{table} 
Table 1: A brief summary of proposed deep learning architectures to segment the prostate on transrectal ultrasound.