  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.
