BoxNet: Deep Learning Based Biomedical Image Segmentation Using Boxes Only Annotation

TL;DR

BoxNet introduces a weakly supervised deep learning approach for biomedical image segmentation that uses only box annotations, combining graph search and deep learning to generate accurate masks with less annotation effort.

Contribution

The paper presents a novel method integrating graph search with deep learning to generate segmentation masks from box annotations, reducing annotation costs in biomedical imaging.

Findings

Achieves nearly the same accuracy as fully supervised methods

Outperforms existing weakly supervised methods

Demonstrates robustness across various biomedical imaging tasks

Abstract

In recent years, deep learning (DL) methods have become powerful tools for biomedical image segmentation. However, high annotation efforts and costs are commonly needed to acquire sufficient biomedical training data for DL models. To alleviate the burden of manual annotation, in this paper, we propose a new weakly supervised DL approach for biomedical image segmentation using boxes only annotation. First, we develop a method to combine graph search (GS) and DL to generate fine object masks from box annotation, in which DL uses box annotation to compute a rough segmentation for GS and then GS is applied to locate the optimal object boundaries. During the mask generation process, we carefully utilize information from box annotation to filter out potential errors, and then use the generated masks to train an accurate DL segmentation network. Extensive experiments on gland segmentation in histology images, lymph node segmentation in ultrasound images, and fungus segmentation in electron microscopy images show that our approach attains superior performance over the best known state-of-the-art weakly supervised DL method and is able to achieve (1) nearly the same accuracy compared to fully supervised DL methods with far less annotation effort, (2) significantly better results with similar annotation time, and (3) robust performance in various applications.