Update paper.md

Paper/paper.md

@@ -46,7 +46,7 @@ aas-journal: Astrophysical Journal <- The name of the AAS journal.
 
 # Summary
 
-Towards the need for automated and precise AI-based analysis of medical images, we present RT-utils, a specialized Python library tuned for the manipulation of radiotherapy (RT) structures stored in DICOM format. RT-utils excels in converting the polygon contours into binary masks, ensuring accuracy and efficiency. By converting DICOM RT structures into standardized formats such as NumPy arrays and SimpleITK Images, RT-utils optimizes inputs for computational solutions such as AI-based automated segmentation techniques or radiomics analysis. Since its inception in 2020, RT-utils has been used extensively with a focus on simplifying complex data processing tasks. RT-utils offers researchers a powerful solution to enhance workflows and drive significant advancements in medical imaging. 
+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.
 
 # Statement of need