A complementary scheme for automated detection of high-uptake regions on dedicated breast PET and whole-body PET/CT.
Natsuki MinouraAtsushi TeramotoAkari ItoOsamu YamamuroMasami NishioKuniaki SaitoHiroshi FujitaPublished in: Radiological physics and technology (2019)
In this study, we aimed to develop a hybrid method for automated detection of high-uptake regions in the breast and axilla using dedicated breast positron-emission tomography (db PET) and whole-body PET/computed tomography (CT) images. In our proposed method, high-uptake regions in the breast and axilla were detected using db PET images and whole-body PET/CT images. In db PET images, high-uptake regions in the breast were detected using adaptive thresholding technique based on the noise characteristics. In whole-body PET/CT images, the region of the breast that includes the axilla was first extracted using CT images. Next, high-uptake regions in the extracted breast region were detected on the PET images. By integration of the results of the two types of PET images, a final candidate region was obtained. In the experiments, the accuracy of extracting the region of the breast and detection ability was evaluated using clinical data. As a result, all breast regions were extracted correctly. The sensitivity of detection was 0.765, and the number of false positive cases were 1.8, which was 30% better than those on whole-body PET/CT alone. These results suggested that the proposed method, combining the two types of PET images is effective for improving detection performance.
Keyphrases
- magnetic resonance
- pet ct
- positron emission tomography
- deep learning
- computed tomography
- convolutional neural network
- optical coherence tomography
- machine learning
- loop mediated isothermal amplification
- dual energy
- high throughput
- artificial intelligence
- squamous cell carcinoma
- single cell
- big data
- lymph node
- neoadjuvant chemotherapy