Endoscopy is a common clinical procedure for the early detection of cancers in hollow organs such as nasopharyngeal cancer, esophageal adenocarcinoma, gastric cancer, colorectal cancer, and bladder cancer. Accurate and temporally consistent localization and segmentation of diseased region-of-interests enable precise quantification and mapping of lesions from clinical endoscopy videos, which is critical for monitoring and surgical planning.
The medical segmentation model is used to classify diseased region-of-interests in the input image. It can be classified into many categories, including BE, cancer, HGD, polyp, and suspicious.
Libmedicalsegmentation is a segmentation library that can be used in the segmentation of multiclass diseases in endoscopy. It offers simple interfaces for developers to deploy segmentation tasks on AMD devices. The following is an example of medical segmentation, where the goal is to mark the diseased region.
The following is an example of semantic segmentation, where the goal is to predict class labels for each pixel in the image.
The following table lists the medical segmentation models supported by the Vitis AI Library.