Segmentation of Prostate Tumour Volumes from PET Images is a Different Ball Game
This work addresses a specific problem in medical image analysis for prostate cancer diagnosis, representing an incremental improvement over existing methods.
The paper tackled the challenge of accurately segmenting prostate tumors from PET images by introducing a custom-feature-clipping normalization technique based on intensity thresholds, which improved U-Net model performance across datasets using two radioactive tracers.
Accurate segmentation of prostate tumours from PET images presents a formidable challenge in medical image analysis. Despite considerable work and improvement in delineating organs from CT and MR modalities, the existing standards do not transfer well and produce quality results in PET related tasks. Particularly, contemporary methods fail to accurately consider the intensity-based scaling applied by the physicians during manual annotation of tumour contours. In this paper, we observe that the prostate-localised uptake threshold ranges are beneficial for suppressing outliers. Therefore, we utilize the intensity threshold values, to implement a new custom-feature-clipping normalisation technique. We evaluate multiple, established U-Net variants under different normalisation schemes, using the nnU-Net framework. All models were trained and tested on multiple datasets, obtained with two radioactive tracers: [68-Ga]Ga-PSMA-11 and [18-F]PSMA-1007. Our results show that the U-Net models achieve much better performance when the PET scans are preprocessed with our novel clipping technique.