Teacher-Student Architecture for Mixed Supervised Lung Tumor Segmentation

TL;DR

This paper presents a teacher-student framework for lung tumor segmentation in CT images that effectively leverages datasets with different supervision levels, reducing annotation effort while maintaining high accuracy.

Contribution

It introduces a novel teacher-student architecture that utilizes mixed supervision data to train lung tumor segmentation models with less labeled data.

Findings

The approach achieves competitive segmentation performance with limited semantic labels.

Models trained on more annotations do not outperform those trained with teacher-annotated data.

The method reduces annotation requirements without degrading accuracy.

Abstract

Purpose: Automating tasks such as lung tumor localization and segmentation in radiological images can free valuable time for radiologists and other clinical personnel. Convolutional neural networks may be suited for such tasks, but require substantial amounts of labeled data to train. Obtaining labeled data is a challenge, especially in the medical domain. Methods: This paper investigates the use of a teacher-student design to utilize datasets with different types of supervision to train an automatic model performing pulmonary tumor segmentation on computed tomography images. The framework consists of two models: the student that performs end-to-end automatic tumor segmentation and the teacher that supplies the student additional pseudo-annotated data during training. Results: Using only a small proportion of semantically labeled data and a large number of bounding box annotated data, we achieved competitive performance using a teacher-student design. Models trained on larger amounts of semantic annotations did not perform better than those trained on teacher-annotated data. Conclusions: Our results demonstrate the potential of utilizing teacher-student designs to reduce the annotation load, as less supervised annotation schemes may be performed, without any real degradation in segmentation accuracy.