Spine Tumor Segmentation Using Deep Learning: A Review

Abstract

The field of medicine has been significantly impacted by technological advancements, particularly in digital imaging and image processing. These advancements have revolutionized early disease detection, computer-aided diagnosis, minimally invasive procedures, and image-guided surgeries. However, medical images, including those of spine tumors, often suffer from challenges such as low contrast, noise, blurriness, and artefacts, which can impede accurate analysis and diagnosis. This paper offers a comprehensive review of spine tumor image segmentation techniques utilizing Deep Learning (DL) as a powerful tool. It explores the crucial role of image segmentation in identifying and delineating specific structures or abnormalities of interest. The review discusses the effectiveness of DL in medical image segmentation, highlighting its ability to learn hierarchical features directly from raw data and its potential to enhance diagnostic value. Additionally, the significance of early detection of spine tumors, the classification of spinal tumors, and the characteristic features of benign tumors are examined. The paper also addresses the limitations of standard MRI protocols in accurately distinguishing between intradural and extradural compartments in certain cases, suggesting the potential need for additional imaging techniques or diagnostic methods. Overall, this review provides valuable insights into the application of DL-based methods for segmenting spine tumor images, contributing to improved diagnosis.