USING THE ADAPTIVERANDOARMASKAUGMENTATION FUNCTION FOR MEDICAL IMAGE AUGMENTATION AND MASK INPAINTING WITH DEEP LEARNING

The segmentation of medical images via deep learning is troubled by two primary issues: lack of segmented data and difficulty of shapes. Modern approaches for data augmentation have clearly surpassed classical techniques like shifting, rotating and flipping in terms of performance. The AdaptiveRandOARMaskAugmentation method implements elastic deformations alongside dynamic deformations and intelligent edge noise injection to develop authentic anatomical changes in training data. This method has been deployed within the MONAI framework because it enables interoperability for deep learning systems. The approach outperformed traditional approaches by obtaining a 7.4% Dice score enhancement alongside a 2.9 mm reduction of HD95 on OpenKBP radiological planning dataset tests. The method retained its 12% accuracy decrease when elastic deformation was severed from testing procedures. The research shows the promising practical value of this approach for radiological planning since it boosts reliable critical organ recognition performance across anatomically comparable areas. The technique solves both incomplete data problems and class distribution problems to improve model outcomes for real-world applications