Context-aware augmentation for liver lesion segmentation: shape uniformity, expansion limit and fusion strategy.

ABSTRACT:

Background

Data augmentation with context has been an effective way to increase the robustness and generalizability of deep learning models. However, to our knowledge, shape uniformity, expansion limit, and fusion strategy of context have yet to be comprehensively studied, particularly in lesion segmentation of medical images.

Methods

To examine the impact of these factors, we take liver lesion segmentation based on the well-known deep learning architecture U-Net as an example and thoroughly vary the context shape, the expansion bandwidth as well as three representative fusion methods. In particular, the context shape includes rectangular, circular and polygonal, the expansion bandwidth is scaled by a maximum value of 2 compared to the lesion size, and the context fusion wei

SUBMITTER: He Q 

PROVIDER: S-EPMC10423356 | biostudies-literature | 2023 Aug

REPOSITORIES: biostudies-literature