Update paper.md

Paper/paper.bib

@@ -98,3 +98,13 @@ @article{Yousefirizi2023-ax
   url       = {https://www.sciencedirect.com/science/article/pii/S1939865424004764},
   author    = {Yousefirizi, Fereshteh and Liyanage, Annudesh and Klyuzhin, Ivan S. and Rahmim, Arman}
 }
+
+@article{oreiller2022head,
+  title={Head and neck tumor segmentation in PET/CT: the HECKTOR challenge},
+  author={Oreiller, Valentin and Andrearczyk, Vincent and Jreige, Mario and Boughdad, Sarah and Elhalawani, Hesham and Castelli, Joel and Valli{\`e}res, Martin and Zhu, Simeng and Xie, Juanying and Peng, Ying and others},
+  journal={Medical image analysis},
+  volume={77},
+  pages={102336},
+  year={2022},
+  publisher={Elsevier}
+}

Paper/paper.md

@@ -83,7 +83,7 @@ RT-utils accelerates deep learning model development and facilitates clinical wo
 
 
 # Real-world Example
-For comparing the effects of different RT-Struct conversion methods, we investigated the RT-utils tool, dcmrtstruct2nii (https://github.com/Sikerdebaard/dcmrtstruct2nii) and the built-in tools from LIFEx[@Nioche2018-ct] and 3D Slicer [@Fedorov2012-ax]. We implemented the conversion technique and conducted a comparison of the NIfTI ground truth files. The level of agreement observed between RT-utils and LIFEx surpasses that of other techniques. The mean absolute errors with respect to RT-utils are shown on sagittal and coronal masks. (Figure 1). The visual inspection of an example of converted masks overlaid on PET scans using different techniques is shown in Figure 1. We presented a real-world example of ground truth conversion from RT-struct to masks, leveraging quantitative metrics such as mean absolute error and pixel count to ensure accuracy and precision in [@Yousefirizi2023-ax].
+For comparing the effects of different RT-Struct conversion methods, we investigated the RT-utils tool, dcmrtstruct2nii (https://github.com/Sikerdebaard/dcmrtstruct2nii), and the built-in tools from LIFEx[@Nioche2018-ct] and 3D Slicer [@Fedorov2012-ax]. We implemented the conversion technique and conducted a comparison of the NIfTI ground truth files. The level of agreement observed between RT-utils and LIFEx surpasses that of other techniques. The mean absolute errors with respect to RT-utils are shown on sagittal and coronal masks. (Figure 1). The visual inspection of an example of converted masks overlaid on PET scans using different techniques is shown in Figure 1. The PET scan used in this figure was sourced from the publicly available HECKTOR Challenge dataset [@oreiller2022head]. We presented a real-world example of ground truth conversion from RT-struct to masks, leveraging quantitative metrics such as mean absolute error and pixel count to ensure accuracy and precision in [@Yousefirizi2023-ax].
 
 
 ![The visual inspection of an example of converted masks overlaid on PET scans using different techniques](../src/RT.png)

README.md

@@ -164,7 +164,9 @@ The add_roi method of our RTStruct class has a multitude of optional parameters
 You can find the comparison between RT-utils and other conversion techniques in this public repository: https://github.com/qurit/dicom_nifti_conversion_project 
 
 ## Examples
-You can find the comparison between RT-utils and other conversion techniques in this public repository: https://github.com/qurit/dicom_nifti_conversion_project 
+You can find the comparison between RT-utils and other conversion techniques in this public repository: https://github.com/qurit/dicom_nifti_conversion_project.
+
+The real-world example in the JOSS paper under review was generated using HECKTOR Challenge data (patient “CHGJ015”) with the code provided in the (https://github.com/qurit/dicom_nifti_conversion_project ) GitHub repository.
 
 ## Contributing
 We welcome contributions to this project! Please review our [CONTRIBUTING guidelines](./CONTRIBUTING.md) for more details.