## 4 Discussion
Inaccurate segmentations due to shadow artifacts may be mitigated with pre-processing strategies proposed by [PERSON] et al. (2020) [39], particularly for TAUS images, where shadow artifacts frequently disrupt prostate boundaries. Conversely, pre-processing training data with 3D Mean, Median, and Gaussian filters appears less effective for improving segmentation outcomes. Although we categorized the existing works into the employment of multi-directional image data, additional shape information, attention mechanisms, and feature map refinement, we recognize the importance of considering additional advanced techniques. Future research should explore knowledge distillation, domain adaptation for multi-center datasets, semi-supervised learning, self-supervised learning, and other emerging methods. These techniques have the potential to significantly enhance the robustness and generalizability of prostate segmentation models in ultrasound imaging. By incorporating these advanced methods, future studies can push the boundaries of what is achievable in prostate ultrasound image segmentation and contribute to more accurate and reliable clinical applications. It is recommended that ablation studies be conducted in which additional models or ensemble networks are implemented. For instance, [PERSON] et al. (2021) [28] designed a DL architecture with multiple systems to enhance segmentation outcomes, but the contribution of each implementation was not explored. Other experiments clearly report which components of proposed DL architectures improve segmentation outcomes. Understanding how added complexity contributes to model performance is essential, especially since efficient DL models are usually required for real-time application. Further exploration of DL for prostate segmentation is warranted, as it allows for volume estimation based on the entire prostate region, which is beneficial for patients suspected of prostate cancer due to the variability in prostate shape across individuals. Additionally, prostate segmentation architectures can be utilized in image registration. The methods reviewed in this article are trained mainly on tumor delineation on ultrasound images without any comparison with the gold standard, PV estimation using MRI segmentation. In this review, only one study, by [PERSON] et al. (2022), focused on an automated PV measurement using TAUS images [43]. While prostate segmentation methods are typically proposed when the PV is estimated on TRUS, the authors automated the workflow of the Ellipsoid formula to obtain the PV by establishing a classification method that predicts the prostate's transverse, anterior-posterior, and longitudinal diameter on TAUS images. In their work, two DL networks were trained separately for the axial and sagittal imaging planes. One network focused on the 2D axial TAUS images to predict outer landmarks related to the transverse and anterior-posterior diameter, and another network was trained on sagittal TAUS images to identify the landmarks of the longitudinal diameter. The plane-specific networks comprised an ensemble of four ResNet-18 CNNs individually trained on image patches containing the prostate in four image scales. Then, the predicted landmarks served as input to utilize the Ellipsoid formula to infer the PV. The proposed method was validated by comparing the Mean Absolute Value Difference (MAVD) between the predicted PV and reference PV measurements manually estimated on TAUS. An average MAVD value of 4.95 mL was obtained, which was smaller than the average inter-expert MAVD value of 5.09 mL. Even though the presented results appear rewarding, there are considerable aspects of the system that warrant discussion. First, the Ellipsoid formula can only be utilized on the mid-axial and mid-sagittal images, where the cross-section of the prostate is at a maximum. Currently, the networks are trained on TAUS images satisfying this criterion, and user interaction and expertise are still required.