New method of using a convolutional neural network for 2D intraprostatic tumor segmentation from PET images

• Published in: Research on biomedical engineering 2023-12, Vol.39 (4), p.905-913
• Main Authors: Rainio, Oona, Lahti, Jari, Anttinen, Mikael, Ettala, Otto, Seppänen, Marko, Boström, Peter, Kemppainen, Jukka, Klén, Riku
• Format: Article
• Language: English
• Subjects: Biomaterials; Biomedical Engineering and Bioengineering; Biomedical Engineering/Biotechnology; Engineering; Original Article
• ISSN: 2446-4740; 2446-4740
• DOI: 10.1007/s42600-023-00314-7

Purpose A new method of using a convolutional neural network (CNN) to perform automatic tumor segmentation from two-dimensional transaxial slices of positron emission tomography (PET) images of high-risk primary prostate cancer patients is introduced. Methods We compare three different methods including (1) usual image segmentation with a CNN whose continuous output is converted to binary labels with a constant threshold, (2) our new technique of choosing separate thresholds for each image PET slice with a CNN to label the pixels directly from the PET slices, and (3) the combination of the two former methods based on using the second CNN to choose the optimal thresholds to convert the output of the first CNN. The CNNs are trained and tested multiple times by using a data set of 864 slices from the PET images of 78 prostate cancer patients. Results According to our results, the Dice scores computed from the predictions of the second method are statistically higher than those of the typical image segmentation ( p -value