Page 49 - AIH-2-1

Artificial Intelligence in Health Asymmetric U-Net for enhanced spinal MRI segmentation

1. Introduction intervention, potentially preventing disease progression
and reducing the burden of chronic spinal conditions on
Spinal diseases are among the most prevalent health issues patients and healthcare systems. Studies have shown that
in modern society. The pathogenesis of spine diagnostics early intervention can significantly improve long-term
involves understanding the underlying mechanisms outcomes for patients with spinal conditions. Accurate
12
and origins of spinal disorders, which is crucial for diagnostics and early interventions can reduce healthcare
accurate diagnosis and effective treatment. Modern spine costs by decreasing the need for extensive surgeries
diagnostics has evolved significantly with advancements and long-term care. Efficient diagnostic processes also
in imaging technology, genetic research, and molecular streamline patient management, optimizing resource
biology, providing deeper insights into spinal pathologies. utilization within healthcare systems.
Pathogenesis in spine diagnostics refers to the study
of how spinal diseases develop and progress. This Accurate diagnosis of spinal conditions is critically
includes degenerative diseases such as osteoarthritis and dependent on the analysis provided by MRI. 9,13-14 High-
intervertebral disc degeneration, as well as inflammatory quality spine MRI segmentation is crucial for enabling
conditions such as ankylosing spondylitis. Degenerative doctors to precisely locate and examine spinal structures,
1
spinal disorders often involve the breakdown of thereby facilitating the diagnosis of various spine-related
intervertebral discs and facet joints. Factors such as aging, diseases such as disc herniation and spinal deformities. 15,16
mechanical stress, and genetic predisposition contribute Accurate segmentation results are essential for assessing
to the degeneration process. Studies have shown that the severity of these conditions and developing effective
mechanical loading and biochemical changes play treatment plans.
2
significant roles in disc degeneration. Diagnostic tools To address the labor-intensive nature of medical
such as magnetic resonance imaging (MRI) and computed imaging tasks, there is a growing trend toward data-
tomography (CT) scans allow for detailed visualization driven approaches in contemporary medical imaging
of these changes. Inflammatory spinal diseases, such as technology. 17,18 Traditional image processing techniques,
3
ankylosing spondylitis, involve chronic inflammation such as thresholding, edge detection, and mathematical
20
19
of the spinal joints, which leads to pain and stiffness. morphology, have yielded some positive outcomes.
21
The pathogenesis of these diseases is linked to genetic However, significant advancements have been made in
markers such as HLA-B27. Advanced diagnostic methods, recent years with the advent of deep learning methods,
4
including MRI and blood tests for inflammatory markers, particularly convolutional neural networks, which have
are essential for early detection and monitoring. Spinal revolutionized spine medical image segmentation. 22,23
5
25
24
tumors’ pathogenesis includes genetic mutations and Models such as U-Net, DeepLab, and Fully
26
environmental factors that lead to abnormal cell growth. Convolutional Networks have been extensively applied
Diagnostic imaging, biopsy, and molecular testing are to spine image segmentation tasks, with the U-Net
crucial in identifying and characterizing spinal tumors, architecture achieving notable success. This model excels
guiding treatment decisions. 6,7 in extracting and representing feature information in MRI
medical image analysis.
The advancements in spine diagnostics have a profound
impact on modern healthcare, influencing clinical practice, To further enhance U-Net’s ability to capture multi-
27
patient outcomes, and healthcare systems. Improved scale information, Huang et al. introduced U-Net++,
imaging technologies, such as high-resolution MRI which incorporates full-scale skip connections. This design
and three-dimensional (3D) CT scans, provide detailed effectively aggregates low-level and high-level semantic
visualization of spinal structures, enhancing diagnostic information, improving segmentation performance.
accuracy. The integration of genetic and molecular However, the practical application of U-Net++ is
8,9
diagnostics enables personalized treatment plans. By challenged by the large size of spine medical images and
understanding the genetic and molecular basis of spinal the network’s complex structure, leading to prolonged
diseases, clinicians can tailor therapies to individual training times and reduced accuracy due to the intricate
patients, improving efficacy and reducing adverse effects. nature of spinal images.
10
Advances in diagnostic imaging have facilitated the This study proposes an innovative and efficient spine
development of minimally invasive surgical techniques. segmentation method called “J-Unet” network to overcome
Real-time imaging guidance during procedures minimizes these limitations and improve model accuracy while
tissue damage, reduces recovery time, and lowers reducing training costs. Our approach includes optimizing
the risk of complications. Early detection of spinal multi-scale skip connection paths and deepening the
11
disorders through advanced diagnostics allows for timely network depth of the upsampling component to capture

Volume 2 Issue 1 (2025) 43 doi: 10.36922/aih.3889