Contour interpolation by deep learning approach.

ABSTRACT:

Purpose

Contour interpolation is an important tool for expediting manual segmentation of anatomical structures. The process allows users to manually contour on discontinuous slices and then automatically fill in the gaps, therefore saving time and efforts. The most used conventional shape-based interpolation (SBI) algorithm, which operates on shape information, often performs suboptimally near the superior and inferior borders of organs and for the gastrointestinal structures. In this study, we present a generic deep learning solution to improve the robustness and accuracy for contour interpolation, especially for these historically difficult cases.

Approach

A generic deep contour interpolation model was developed and trained using 16,796 publicly available cases from 5 diff

SUBMITTER: Zhao C 

PROVIDER: S-EPMC9768435 | biostudies-literature | 2022 Nov

REPOSITORIES: biostudies-literature