Update paper.md

Paper/paper.md

@@ -46,11 +46,11 @@ aas-journal: Astrophysical Journal <- The name of the AAS journal.
 
 # Summary
 
-In the pursuit of automated and precise analysis of medical images using artificial intelligence, we introduce RT-utils, a specialized Python library designed to simplify the handling of radiotherapy imaging data. Medical images are commonly stored in the DICOM standard (Digital Imaging and Communications in Medicine), which is the universal format for sharing medical imaging information. In radiotherapy, the region of interests (ROIs) around the critical structures like tumors and surrounding organs are represented as detailed contours within DICOM files, specifically in what is known as the RTSTRUCT files (Radiotherapy Structure). RT-utils excels at converting these complex polygonal contours into straightforward binary masks. These masks are essentially grids where each point indicates the presence or absence of a structure, making them ideal for computational processing. By transforming DICOM radiotherapy structures into standardized data formats like NumPy arrays and SimpleITK images, RT-utils streamlines the input for AI-based segmentation techniques and radiomics analysis, which are methods used to extract quantitative features from medical images. Since its inception in 2020, RT-utils has been widely adopted to simplify complex data processing tasks in medical imaging. It offers researchers and developers a powerful tool to enhance their workflows, ultimately driving significant advancements in medical image analysis and contributing to improved patient care.
+In the pursuit of automated and precise analysis of medical images using artificial intelligence, we introduce RT-utils, a specialized Python library designed to simplify the handling of radiotherapy imaging data. Medical images are commonly stored in the DICOM standard (Digital Imaging and Communications in Medicine), which is the universal format for sharing medical imaging information. In radiotherapy, the region of interests (ROIs) around the critical structures like tumors and surrounding organs are represented as detailed contours within DICOM files, specifically in what is known as the RTSTRUCT files (Radiotherapy Structure). RT-utils excels at converting these complex polygonal contours into straightforward binary masks. These masks are essentially grids where each point indicates the presence or absence of a structure, making them ideal for computational processing. By transforming DICOM radiotherapy structures into standardized data formats like NumPy arrays and SimpleITK images, RT-utils streamline the input for AI-based segmentation techniques and radiomics analysis, which are methods used to extract quantitative features from medical images. Since its inception in 2020, RT-utils has been widely adopted to simplify complex data processing tasks in medical imaging. It offers researchers and developers a powerful tool to enhance their workflows, ultimately driving significant advancements in medical image analysis and contributing to improved patient care.
 
 # Statement of need
 
-The increasing adoption of AI-based methods for medical image analysis necessitates efficient tools for handling DICOM images and RT-Structures. While existing software packages provide basic functionalities for data conversion, they often lack advanced features required for seamless integration into clinical workflows. The growing need for automated and robust analysis of medical images has driven the adoption of AI-based methods that often use DICOM images and RT structures as masks. However, the effectiveness of these AI approaches can vary due to differences in data sources and conversion techniques [@Whybra2023-en][@Yousefirizi2023-ax][@Rufenacht2023-as]. Despite the availability of tools for converting DICOM images and RT-Structures into other formats [@Rufenacht2023-as][@Anderson2021-fp], integrating auto-segmentation solutions using deep learning in clinical environments is rare due to the lack of open-source frameworks that handle DICOM RT-Structure sets effectively. Software packages like dcmrtstruct2nii, DicomRTTool [@Anderson2021-fp], and PyRaDiSe [@Rufenacht2023-as] provide necessary functionalities, while frameworks like TorchIO [@Perez-Garcia2021-jf] and MONAI [@Creators_The_MONAI_Consortium_undated-or] face limitations in processing DICOM RT-structure data. Research has shown that variations in mask generation methods affect patient clustering and radiomic-based modeling in multi-center studies [@Whybra2023-en]. RT-utils addresses this gap by offering a specialized Python library that enhances the efficiency of manipulating RT-Structures. It is designed for researchers and clinicians who require advanced yet user-friendly tools to: i) Convert and manipulate RT-Struct data with precision. ii) Integrate AI-generated segmentation masks into clinical DICOM formats. iii) Streamline workflows by automating repetitive and complex tasks. iv) Ensure compatibility with clinical systems through meticulous DICOM header management. By providing these capabilities, RT-utils optimizes workflows in medical imaging analysis, facilitating the translation of AI models from research to clinical practice. RT-utils offers advanced techniques to convert expert-provided contours and AI tool output masks to RT-struct format, making them suitable for clinical workflows.
+The increasing adoption of AI-based methods for medical image analysis necessitates efficient tools for handling DICOM images and RT-Structures. While existing software packages provide basic functionalities for data conversion, they often lack the advanced features required for seamless integration into clinical workflows. The growing need for automated and robust analysis of medical images has driven the adoption of AI-based methods that often use DICOM images and RT structures as masks. However, the effectiveness of these AI approaches can vary due to differences in data sources and conversion techniques [@Whybra2023-en][@Yousefirizi2023-ax][@Rufenacht2023-as]. Despite the availability of tools for converting DICOM images and RT-Structures into other formats [@Rufenacht2023-as][@Anderson2021-fp], integrating auto-segmentation solutions using deep learning in clinical environments is rare due to the lack of open-source frameworks that handle DICOM RT-Structure sets effectively. Software packages like dcmrtstruct2nii, DicomRTTool [@Anderson2021-fp], and PyRaDiSe [@Rufenacht2023-as] provide necessary functionalities, while frameworks like TorchIO [@Perez-Garcia2021-jf] and MONAI [@Creators_The_MONAI_Consortium_undated-or] face limitations in processing DICOM RT-structure data. Research has shown that variations in mask-generation methods affect patient clustering and radiomic-based modeling in multi-center studies [@Whybra2023-en]. RT-utils addresses this gap by offering a specialized Python library that enhances the efficiency of manipulating RT-Structures. It is designed for researchers and clinicians who require advanced yet user-friendly tools to: i) Convert and manipulate RT-Struct data with precision. ii) Integrate AI-generated segmentation masks into clinical DICOM formats. iii) Streamline workflows by automating repetitive and complex tasks. iv) Ensure compatibility with clinical systems through meticulous DICOM header management. By providing these capabilities, RT-utils optimizes workflows in medical imaging analysis, facilitating the translation of AI models from research to clinical practice. RT-utils offers advanced techniques to convert expert-provided contours and AI tool output masks to RT-struct format, making them suitable for clinical workflows.
 
 # Overview of RT-utils