Advances in Deep Learning Methods for Prostate Segmentation and Volume Estimation in Ultrasound Imaging  
[PERSON]  
1 Image-Guided Surgery, Department of Surgery, Netherlands Cancer Institute, Plesmanlaan 121, 1066 CX Amsterdam, The Netherlands; [EMAIL_ADDRESS] (L.M.K. ); [EMAIL_ADDRESS] (T.N. ); [EMAIL_ADDRESS] (M.F.) [PERSON]  
1 Image-Guided Surgery, Department of Surgery, Netherlands Cancer Institute, Plesmanlaan 121, 1066 CX Amsterdam, The Netherlands; [EMAIL_ADDRESS] (L.M.K. ); [EMAIL_ADDRESS] (T.N. ); [EMAIL_ADDRESS] (M.F.) [PERSON]  
1 Image-Guided Surgery, Department of Surgery, Netherlands Cancer Institute, Plesmanlaan 121, 1066 CX Amsterdam, The Netherlands; [EMAIL_ADDRESS] (L.M.K. ); [EMAIL_ADDRESS] (T.N. ); [EMAIL_ADDRESS] (M.F.) [PERSON]  
1 Image-Guided Surgery, Department of Surgery, Netherlands Cancer Institute, Plesmanlaan 121, 1066 CX Amsterdam, The Netherlands; [EMAIL_ADDRESS] (L.M.K. ); [EMAIL_ADDRESS] (T.N. ); [EMAIL_ADDRESS] (M.F.) Footnote 1: [[https://www.mdpi.com/journal/applsci](https://www.mdpi.com/journal/applsci)]([https://www.mdpi.com/journal/applsci](https://www.mdpi.com/journal/applsci))  
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often preferred for its non-invasive and more comfortable approach [9]. Figure 1 illustrates examples of prostate images obtained by TAUS, TRUS, and MRI, highlighting the differences among these imaging modalities. Estimating PV with US is particularly valuable for early-stage diagnosis. However, accurate PV estimation using US is challenging due to the complexities in interpreting US images. Tasks like prostate segmentation and annotation often lead to significant variability and inaccuracies [10]. This is primarily due to poor contrast between the prostate and surrounding tissues, along with imaging artifacts such as echoic shadows and calcifications [11]. Additionally, TAUS faces unique challenges, including difficulties in observing the prostate when shadow artifacts disrupt the prostate boundary due to ultrasonic reflection by the pelvic bone [12]. To address these challenges, computer-aided methods have been developed to reduce clinician workload, shorten examination times, and improve segmentation and annotation accuracy. There has been a growing interest in automated prostate segmentation methods to derive PV. Traditional methods such as deformable models, edge-based segmentation, and region-based segmentation have been extensively reviewed [11; 13]. These methods rely heavily on extracting prostate-specific image features, which are often compromised by contrast variations and artifacts in US images [14]. In recent years, deep learning (DL) architectures have emerged as promising alternatives for automatic prostate assessment. DL models can process entire images and automatically extract multi-level features, capturing both semantic and detailed image information [15]. Despite these advancements, DL methods still face challenges in accurately delineating prostate boundaries [16].