Minimally interactive segmentation of soft-tissue tumors on CT and MRI using deep learning.

ABSTRACT:

Background

Segmentations are crucial in medical imaging for morphological, volumetric, and radiomics biomarkers. Manual segmentation is accurate but not feasible in clinical workflow, while automatic segmentation generally performs sub-par.

Purpose

To develop a minimally interactive deep learning-based segmentation method for soft-tissue tumors (STTs) on CT and MRI.

Material and methods

The interactive method requires the user to click six points near the tumor's extreme boundaries in the image. These six points are transformed into a distance map and serve, with the image, as input for a convolutional neural network. A multi-center public dataset with 514 patients and nine STT phenotypes in seven anatomical locations, with CT or T1-weighted MRI, was used for training

SUBMITTER: Spaanderman DJ 

PROVIDER: S-EPMC12021718 | biostudies-literature | 2025 May

REPOSITORIES: biostudies-literature