Interactive Image Selection and Training for Brain Tumor Segmentation Network

TL;DR

This paper presents an interactive image selection and training method based on FLIM for brain tumor segmentation, reducing the need for large annotated datasets and achieving high performance with fewer images.

Contribution

It introduces an interactive approach leveraging user knowledge for image selection in FLIM-based training, improving segmentation performance with fewer training images.

Findings

Achieved comparable or better segmentation with fewer images.

Demonstrated the effectiveness of user-guided image selection.

Reduced annotation effort for brain tumor segmentation.

Abstract

Medical image segmentation is a relevant problem, with deep learning being an exponent. However, the necessity of a high volume of fully annotated images for training massive models can be a problem, especially for applications whose images present a great diversity, such as brain tumors, which can occur in different sizes and shapes. In contrast, a recent methodology, Feature Learning from Image Markers (FLIM), has involved an expert in the learning loop, producing small networks that require few images to train the convolutional layers. In this work, We employ an interactive method for image selection and training based on FLIM, exploring the user's knowledge. The results demonstrated that with our methodology, we could choose a small set of images to train the encoder of a U-shaped network, obtaining performance equal to manual selection and even surpassing the same U-shaped network trained with backpropagation and all training images.