Update paper.md

Paper/paper.md

@@ -54,14 +54,14 @@ The increasing adoption of AI-based methods for medical image analysis necessita
 
 # Overview of RT-utils
 
-Our module introduces intuitive techniques for efficient data curation of RT-Structure files, facilitating the identification of distinct region of interest (ROI) names and their corresponding locations within the structures. It adeptly handles scenarios where multiple ROI names correspond to the same structure, ensuring a comprehensive and accurate representation. Additionally, the module offers the conversion of DICOM images and RT-Struct into widely used formats such as NumPy arrays and SimpleITK Images. These standardized formats serve as optimal inputs for various applications, including deep learning models, image analysis, and radiomic feature calculations (extraction). Moreover, the toolkit simplifies the process of creating DICOM RT-Struct from predicted NumPy arrays, commonly the outputs of semantic segmentation deep learning models, providing a versatile solution for researchers and practitioners in medical imaging.
+Our library introduces intuitive techniques for efficient data curation of RT-Structure files, facilitating the identification of distinct region of interest (ROI) names and their corresponding locations within the structures. It adeptly handles scenarios where multiple ROI names correspond to the same structure, ensuring a comprehensive and accurate representation. Additionally, the library offers the conversion of DICOM images and RT-Struct into widely used formats such as NumPy arrays and SimpleITK Images. These standardized formats serve as optimal inputs for various applications, including deep learning models, image analysis, and radiomic feature calculations (extraction). Moreover, the toolkit simplifies the process of creating DICOM RT-Struct from predicted NumPy arrays, commonly the outputs of semantic segmentation deep learning models, providing a versatile solution for researchers and practitioners in medical imaging.
 In the realm of data science, discretized image formats such as NIfTI, NRRD, and MHA are commonly employed, while radiotherapy workflows heavily rely on the DICOM format, specifically the DICOM RT-Struct. Unlike data science architectures like U-Net, which operate on grid-based data, handling the continuously spaced contour points present in RT-Struct poses a unique challenge. To bridge this gap, accurate data conversion between discrete and continuous spaces becomes crucial when working with clinical DICOM RT-Struct data.
 
 ## Technical Overview 
 
 **RT-utils** streamlines RT-Structure file curation by identifying Regions of Interest (ROIs), handling multiple ROI names for the same structure, and converting DICOM RT-Struct and images into formats like NumPy arrays and SimpleITK images. These standardized outputs enable deep learning, image analysis, and radiomics applications. The toolkit also generates DICOM RT-Struct files from predicted arrays, bridging segmentation models and clinical workflows.
 
-The module addresses challenges in converting between discretized image formats (e.g., NIfTI, MHA) and RT-Struct's contour-based format, leveraging geometric operations for accurate binary masks from contour points.
+The library addresses challenges in converting between discretized image formats (e.g., NIfTI, MHA) and RT-Struct's contour-based format, leveraging geometric operations for accurate binary masks from contour points.
 
 Since its 2020 release, RT-utils has grown steadily, with its GitHub repository (*Starred >180 times*) and PyPI presence facilitating collaborative development. Installation is simple: `pip install RT-utils`.