Kidney Tumor Image Segmentation based on nnUNetV2

With the rapid advancement of computer technology, its applications in the medical industry, particularly in computer-aided medical technology, have experienced substantial growth and development. Kidney tumors, a prevalent urological ailment, demand early detection and precise treatment to enhance patient survival rates and decrease mortality rates effectively. However, the intricate morphology and tissue similarity of early-stage kidney tumors pose significant challenges, complicating their prompt diagnosis. Moreover, the scarcity of specialized medical professionals proficient in diagnosing kidney tumors highlights the pressing necessity for a rapid and precise computer-aided diagnostic system. To effectively address this issue, we meticulously devised a renal tumor segmentation methodology centered around nnUNetV2, making use of the KiTS19 dataset comprising CT scans from 300 individuals afflicted with renal cancer. Our comprehensive approach encompasses crucial data preprocessing steps such as cropping, resampling, and normalization, leveraging the nnUNetV2 framework to tailor U-Net configurations, and implementing a meticulous training strategy integrating five-fold cross-validation and data augmentation techniques such as random rotation, scaling, and elastic deformation. Incorporating optimization strategies like patch-based inference and test-time augmentation further bolsters segmentation accuracy and operational efficiency. Empirical results unequivocally demonstrate that our model achieved notable segmentation accuracy, aligning closely with the stringent clinical prerequisites for the diagnosis and treatment of renal tumors.