Efficient contour-based annotation by iterative deep learning for organ segmentation from volumetric medical images.

ABSTRACT:

Purpose

Training deep neural networks usually require a large number of human-annotated data. For organ segmentation from volumetric medical images, human annotation is tedious and inefficient. To save human labour and to accelerate the training process, the strategy of annotation by iterative deep learning recently becomes popular in the research community. However, due to the lack of domain knowledge or efficient human-interaction tools, the current AID methods still suffer from long training time and high annotation burden.

Methods

We develop a contour-based annotation by iterative deep learning (AID) algorithm which uses boundary representation instead of voxel labels to incorporate high-level organ shape knowledge. We propose a contour segmentation network with a multi-

SUBMITTER: Zhuang M 

PROVIDER: S-EPMC9889459 | biostudies-literature | 2023 Feb

REPOSITORIES: biostudies-literature