# Estimation of the Prostate Volume from Abdominal Ultrasound Images by Image-Patch Voting
## 2 Previous Work
Patient-specific Gabor features were used to train kernel support vector machines and segment newly acquired TRUS images. [PERSON] et al. [29] trained a set of wavelet support vector machines to adaptively capture features of the US images to differentiate the prostate and non-prostate tissue. The intensity profiles around the boundary were compared to the prostate model. The segmented prostate was updated and compared to the shape model until convergence. [PERSON] et al. [30] built multiple mean parametric models derived from principal component analysis of shape and posterior probabilities in a multi-resolution framework. With the development of deep-learning methods, feature-extraction tasks moved from the human side to the algorithm side. This allowed experts in many areas to use deep learning for their studies. [PERSON] et al. [31] formulated the prostate boundary sequentially and explored sequential clues using RNNs to learn the shape knowledge. [PERSON] et al. [32] used a 3D deeply supervised V-Net to deal with the optimization difficulties when training a deep network with limited training data. [PERSON] et al. [33] trained a CNN ensemble that uses the disagreement among this ensemble to identify uncertain segmentations to estimate a segmentation-uncertainty map. Then uncertain segmentations were improved by utilizing the prior shape information. [PERSON] et al. [34] used attention modules to exploit the complementary information encoded in different layers of CNN. This mechanism suppressed the non-prostate noise at shallow layers and increased more prostate details into features at deep layers of the CNN. [PERSON] et al. [35] modified the expansion section of the standard U-Net to reduce over-fitting and improve performance. In our previous work, [14], we implemented a part-based approach to detect the prostate and its bounding box. The system was built on a deformable model of the prostate and adjacent structures. In another previous work, [15], we used concatenated image patches at different scales and trained a model with a single network. There was a voting process for the whole prostate boundary and layers parallel to the boundary. In this study, we extended our previous work to use a new patch mechanism with a new model. Additionally, we have MR annotations corresponding to AUS samples in our experiments, which will be used for comparison with golden volume standard. Fully automated radiology measurement systems are a topic of discussion in healthcare. Most of these systems are designed in an end-to-end fashion [36; 37] that complicates the expert-in-the-loop solutions that are more compatible with experts' normal workflow. Clinicians often need to know how outputs are produced to trust the system [38]. It is not easy to examine the results of end-to-end systems because they are too complex to be understood and explained by many [39]. The operation of these systems should be transparent so that they can be explained and interpreted by experts [40]. The combination of artificial and human intelligence in medical analysis would outperform the analysis of fully automated systems or humans [41], while being faster than traditional systems [42]. Our model addresses these issues by following the classical prostate-volume-estimationprocess. The resulting system yields intermediate results that allow manual intervention and are explainable for experts. It also produces final results allowing fully automatic use.