Medical Segmentation - 1.4.1 English

Vitis AI Library User Guide (UG1354)

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1.4.1 English

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 which can be used in segmentation of multi-class diseases in endoscopy. It offers simple interfaces for developers to deploy segmentation tasks on Xilinx FPGAs. The following is an example of medical segmentation, where the goal is to mark the diseased region.

Figure 1. Marking 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.

Figure 2. Medical Segmentation Example

The following table lists the medical segmentation models supported by the Vitis AI Library.

Table 1. Semantic Segmentation Models
No Model Name Framework
1 FPN_Res18_Medical_segmentation Caffe