Biomedical Image Segmentation: A Systematic Literature Review of Deep Learning Based Object Detection Methods

TL;DR

This systematic review analyzes 148 studies on deep learning object detection methods for biomedical image segmentation, highlighting key models, challenges, and future research directions to advance medical image analysis.

Contribution

It provides a comprehensive, standardized analysis of deep learning detection techniques in biomedical segmentation, including detailed categorization and critical evaluation of methods.

Findings

Analysis of two-stage, one-stage, and point-based detection models

Identification of key challenges in biomedical image segmentation

Discussion of future research directions and potential benefits

Abstract

Biomedical image segmentation plays a vital role in diagnosis of diseases across various organs. Deep learning-based object detection methods are commonly used for such segmentation. There exists an extensive research in this topic. However, there is no standard review on this topic. Existing surveys often lack a standardized approach or focus on broader segmentation techniques. In this paper, we conducted a systematic literature review (SLR), collected and analysed 148 articles that explore deep learning object detection methods for biomedical image segmentation. We critically analyzed these methods, identified the key challenges, and discussed the future directions. From the selected articles we extracted the results including the deep learning models, targeted imaging modalities, targeted diseases, and the metrics for the analysis of the methods. The results have been presented in tabular and/or charted forms. The results are presented in three major categories including two stage detection models, one stage detection models and point-based detection models. Each article is individually analyzed along with its pros and cons. Finally, we discuss open challenges, potential benefits, and future research directions. This SLR aims to provide the research community with a quick yet deeper understanding of these segmentation models, ultimately facilitating the development of more powerful solutions for biomedical image analysis.