# Estimation of the Prostate Volume from Abdominal Ultrasound Images by Image-Patch Voting
## 4 Experiments and Results
### Data Set and Manual Annotations on AUS and MR Images
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \(MEAN\) & \(AUS_{exp1}^{mark1}\) & \(AUS_{exp1}^{mark2}\) & \(AUS_{exp2}^{mark1}\) & \(AUS_{exp2}^{mark2}\) & \(MR_{exp1}^{mark1}\) & \(MR_{exp1}^{mark2}\) & \(MR_{exp2}^{mark1}\) & \(MR_{exp2}^{mark2}\) \\ \hline \(AUS_{exp1}^{mark1}\) & & 3.46 & 5.19 & 5.44 & 7.70 & 7.20 & 6.36 & 7.12 \\ \(AUS_{exp1}^{mark2}\) & 3.46 & & 4.76 & 4.89 & 8.67 & 7.96 & 6.43 & 7.06 \\ \(AUS_{exp2}^{mark1}\) & 5.19 & 4.76 & & 4.01 & 9.53 & 9.08 & 7.15 & 8.05 \\ \(AUS_{exp2}^{mark2}\) & 5.44 & 4.89 & 4.01 & & 9.68 & 9.26 & 6.93 & 7.38 \\ \(MR_{exp1}^{mark1}\) & 7.70 & 8.67 & 9.53 & 9.68 & & 2.63 & 4.96 & 5.48 \\ \(MR_{exp1}^{mark2}\) & 7.20 & 7.96 & 9.08 & 9.26 & 2.63 & & 4.79 & 4.92 \\ \(MR_{exp2}^{mark1}\) & 6.36 & 6.43 & 7.15 & 6.93 & 4.96 & 4.79 & & 3.00 \\ \(MR_{exp2}^{mark2}\) & 7.12 & 7.06 & 8.05 & 7.38 & 5.48 & 4.92 & 3.00 & \\ \hline \(AUS_{QN}\) & 4.86 & 4.52 & 5.08 & 5.37 & 7.10 & 6.55 & 5.37 & 5.86 \\ \(AUS_{BL}\) & 6.62 & 6.13 & 5.79 & 6.46 & 7.92 & 7.27 & 5.97 & 5.77 \\ \hline \end{tabular}
\end{table} 
Table 1: Top part shows intra-expert and inter-expert MAVD values in cm\({}^{3}\) for AUS and MR modalities and is symmetrical. The MAVD values between experts, our QDCNN system, and the baseline is shown in the bottom part. Green shows the smallest (best), and red shows the highest MAVD. See the text for the explanation of the column and row headings. Table 1 shows that the average intra-expert MAVD values for MR images was 2.81 (\(\frac{2.63+3.00}{2}\)) cm\({}^{3}\), while it was 3.73 (\(\frac{3.46+4.01}{2}\)) cm\({}^{3}\) for AUS images. These results show that human experts' volume estimations can vary at different marking sessions, even for the MR modality, which is the gold standard. It is expectedly normal that there is a greater intra-expert MAVD for the AUS modality due to lower SNR values and other image-quality problems. The average intra-expert MAVD was 7.63 (\(\frac{7.70+7.20+8.67+7.96+7.15+8.05+6.93+7.38}{8}\)) cm\({}^{3}\) between the modalities MR and AUS. Considering MR annotations as the gold standard, we can see that manual AUS annotations cause greater MAVD values. When examining inter-expert MAVD values, we encountered greater values for both intra-modality and inter-modality comparisons. As shown in Table 1, we obtained average values of 5.03 (\(\frac{4.96+5.48+4.79+4.92}{4}\)) and 5.07 (\(\frac{5.19+5.41+4.76+4.89}{4}\)) cm\({}^{3}\) inter-expert MAVD for MR-MR and AUS-AUS comparisons, respectively. That shows us that manual annotations by different experts cause greater MAVD for both MR and AUS modalities. When we considered different modalities for inter-expert MAVD, we obtained an average of 8.06 (\(\frac{6.36+7.12+6.43+7.06+9.53+9.08+9.68+9.26}{8}\)) cm\({}^{3}\) value from which we inferred that manual annotations have a high MAVD between AUS images and the gold standard. The comparison of the manually marked images shows that there is always a difference between different experts. Similarly, the same expert will mark different positions at different marking sessions. As a result, besides other benefits mentioned before, the guidance of the automated system is expected to enhance the consistency and the stability of the volume-estimation results by the experts. The following section shows our system's guidance ability, comparing the system and the expert volume estimations.