AI and Deep Learning for Automated Segmentation and Quantitative Measurement of Spinal Structures in MRI

Background: Accurate spinal structure measurement is crucial for assessing spine health and diagnosing conditions like spondylosis, disc herniation, and stenosis. Manual methods for measuring intervertebral disc height and spinal canal diameter are subjective and time-consuming. Automated solutions are needed to improve accuracy, efficiency, and reproducibility in clinical practice. Purpose: This study develops an autonomous AI system for segmenting and measuring key spinal structures in MRI scans, focusing on intervertebral disc height and spinal canal anteroposterior (AP) diameter in the cervical, lumbar, and thoracic regions. The goal is to reduce clinician workload, enhance diagnostic consistency, and improve assessments. Methods: The AI model leverages deep learning architectures, including UNet, nnU-Net, and CNNs. Trained on a large proprietary MRI dataset, it was validated against expert annotations. Performance was evaluated using Dice coefficients and segmentation accuracy. Results: The AI model achieved Dice coefficients of 0.94 for lumbar, 0.91 for cervical, and 0.90 for dorsal spine segmentation (D1-D12). It precisely measured spinal parameters like disc height and canal diameter, demonstrating robustness and clinical applicability. Conclusion: The AI system effectively automates MRI-based spinal measurements, improving accuracy and reducing clinician workload. Its consistent performance across spinal regions supports clinical decision-making, particularly in high-demand settings, enhancing spinal assessments and patient outcomes.

Deep Learning-Based Automated Workflow for Accurate Segmentation and Measurement of Abdominal Organs in CT Scans

Background: Automated analysis of CT scans for abdominal organ measurement is crucial for improving diagnostic efficiency and reducing inter-observer variability. Manual segmentation and measurement of organs such as the kidneys, liver, spleen, and prostate are time-consuming and subject to inconsistency, underscoring the need for automated approaches. Purpose: The purpose of this study is to develop and validate an automated workflow for the segmentation and measurement of abdominal organs in CT scans using advanced deep learning models, in order to improve accuracy, reliability, and efficiency in clinical evaluations. Methods: The proposed workflow combines nnU-Net, U-Net++ for organ segmentation, followed by a 3D RCNN model for measuring organ volumes and dimensions. The models were trained and evaluated on CT datasets with metrics such as precision, recall, and Mean Squared Error (MSE) to assess performance. Segmentation quality was verified for its adaptability to variations in patient anatomy and scanner settings. Results: The developed workflow achieved high precision and recall values, exceeding 95 for all targeted organs. The Mean Squared Error (MSE) values were low, indicating a high level of consistency between predicted and ground truth measurements. The segmentation and measurement pipeline demonstrated robust performance, providing accurate delineation and quantification of the kidneys, liver, spleen, and prostate. Conclusion: The proposed approach offers an automated, efficient, and reliable solution for abdominal organ measurement in CT scans. By significantly reducing manual intervention, this workflow enhances measurement accuracy and consistency, with potential for widespread clinical implementation. Future work will focus on expanding the approach to other organs and addressing complex pathological cases.