Machine Learning Techniques for Biomedical Image Segmentation: An Overview of Technical Aspects and Introduction to State-of-Art Applications

TL;DR

This paper reviews recent advances in machine learning techniques, including classical and deep learning models, for biomedical image segmentation, highlighting their applications, successes, limitations, and training challenges.

Contribution

It provides a comprehensive overview of both classical and deep learning methods for biomedical image segmentation, emphasizing recent developments and practical challenges.

Findings

Deep learning models outperform classical methods in accuracy.

Classical models are more sample-efficient and less complex.

Training challenges include data scarcity and model optimization issues.

Abstract

In recent years, significant progress has been made in developing more accurate and efficient machine learning algorithms for segmentation of medical and natural images. In this review article, we highlight the imperative role of machine learning algorithms in enabling efficient and accurate segmentation in the field of medical imaging. We specifically focus on several key studies pertaining to the application of machine learning methods to biomedical image segmentation. We review classical machine learning algorithms such as Markov random fields, k-means clustering, random forest, etc. Although such classical learning models are often less accurate compared to the deep learning techniques, they are often more sample efficient and have a less complex structure. We also review different deep learning architectures, such as the artificial neural networks (ANNs), the convolutional neural networks (CNNs), and the recurrent neural networks (RNNs), and present the segmentation results attained by those learning models that were published in the past three years. We highlight the successes and limitations of each machine learning paradigm. In addition, we discuss several challenges related to the training of different machine learning models, and we present some heuristics to address those challenges.