# Estimation of the Prostate Volume from Abdominal Ultrasound Images by Image-Patch Voting
## 5 Conclusions  
Radiologists often desire computerized radiology systems with an expert-in-the-loop structure in their everyday workflow, but the popular end-to-end systems are difficult to adapt for such employment. We proposed an image-patch voting system to automate the commonly used ellipsoid formula-based prostate-volume-estimation method. Experts can see the detected endpoints of the ellipsoid diameters and change the endpoint positions if necessary, providing explainability and confidence in the final measurement results. We verified the effectiveness of the image-patch voting method against a common baseline model.  
Since some of our sample patients had both AUS and MR images, we had the chance to compare our system's AUS volume estimations with the gold standard. By comparing both our system and experts to the gold standard, we showed that our system's volume estimations fall within the expert estimations. The markings made by two experts in two different marking sessions showed unignorable intra-expert and inter-expert MAVD values in the estimations made on both the same and different modalities. On the other hand, the MAVD values of our system, which were less than inter-expert MAVD values on AUS images and intra- and inter-expert MAVD values on different modalities, indicate the good level of guidance ability of the proposed method. Our system can help to enhance expert volume estimations' stability and consistency.  
The new data set we created is valuable for further work on AUS images in automated medical-image analysis. To our knowledge, the data set is the first to include expert markings on both AUS and MR images of sample patients in two different marking sessions. Supplementary material, including the data set, the expert markings, and the project code, is  
Figure 11: Examples of t-SNE graphs of the feature vectors from the QDCNN, the \(64\times 64\) pixels ResNet-18, and the \(128\times 128\) pixels ResNet-18 for the distance tasks.  
available for public use at [[https://github.com/nurbalbayrak/prostate_volume_estimation](https://github.com/nurbalbayrak/prostate_volume_estimation)]([https://github.com/nurbalbayrak/prostate_volume_estimation](https://github.com/nurbalbayrak/prostate_volume_estimation)) (accessed on 23 January 2022).  
Future work might apply the proposed model and patch structure to other modalities. Hybrid modalities [48] might be a good supply of data for a multi-patch system like the proposed one. Statistical analysis might be done to test statistical MAVD differences among different groups.  
Writing--original draft preparation, N.B.A.; writing--review and editing, Y.S.A. All authors have read and agreed to the published version of the manuscript.  
This study was supported by TUBITAK project 114E536.  
The study was conducted according to the guidelines of the Declaration of Helsinki and approved by the Ethics Committee of MALTEPE UNIVERSITY (protocol code 14 and date of approval 19 March 2014).  
Informed Consent Statement: Informed consent was obtained from all subjects involved in the study.  
The data set, the expert markings, and the project code, is available for public use at [[https://github.com/nurbalbayrak/prostate_volume_estimation](https://github.com/nurbalbayrak/prostate_volume_estimation)]([https://github.com/nurbalbayrak/prostate_volume_estimation](https://github.com/nurbalbayrak/prostate_volume_estimation)) (accessed on 23 January 2022).  
We acknowledge [PERSON], [PERSON], [PERSON], [PERSON], and [PERSON] from Maltepe University the Hospital of Medical School; [PERSON] from Istanbul Medeniyet University; and [PERSON] from Kartal Lutfi Kurdar City Hospital for providing data and annotations.  
The authors declare no conflict of interest.