Second post in a series of posts written by members of the Qurit Team. Written by Fereshteh Yousefi Rizi, PhD.
With the growing importance of PET/CT scans in cancer care, the incentives for research to develop methods for PET/CT image enhancement, classification, and segmentation have been increasing in recent years. PET/CT analysis methods have been developed to utilize the functional and metabolic information of PET images and anatomical localization of CT images. Although tumor boundaries in PET and CT images are not always matched , in order to use the complementary information of PET and CT modalities, existing PET/CT segmentation methods either process PET and CT images separately or simultaneously to achieve accurate tumor segmentation .
Regarding standardized, reproducible, and automatic PET/CT tumor segmentation, there are still difficulties for clinical applications [1, 3]. The relatively low resolution of PET images as well as noise and possible heterogeneity of tumor regions are limitations to this end. Besides, PET-CT registration (even with hardware registration) may cause some errors due to patient motion . Moreover, fusion of complementary information is problematic for PET and CT images [2, 5]. To address these issues, segmentation techniques in PET/CT images can be categorized into two categories :
- Combining modality-specific features that are extracted separately from PET and CT images [6-9]
- Fusion of complementary features from CT and PET modalities that are prioritized for different tasks [2, 5, 10-13].
Figure 1 depicts overall scheme of a proposed method by Teramoto et al.  to separately identify tumor region on the PET and CT images. Another proposed PET/CT co-segmentation method by Zhong et al.  is shown in Figure 2 adapted from modality-specific encoder branches.
Deep learning solutions such as V-net , W-net , and generative adversarial network(GAN)  have gained much attention recently for medical image segmentation . Accessing sufficient amount of annotated data, deep models outperform majority of conventional segmentation methods without the need for expert-designed features [1, 18]. Furthermore, there are some ongoing studies on using unsupervised and weakly supervised models  or noisy labels  and also scarce annotation  to make the solutions less dependent on the radiologists.
I am currently working on developing new techniques to improve tumor segmentation results, benefiting from PET/CT complementary information and considering the use of less annotated data, in order to significantly improve predictive models in cancer.
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