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

TL;DR

This work presents an end-to-end automated workflow for segmenting and measuring key abdominal organs in CT scans using state-of-the-art deep-learning models: nnU-Net, U-Net++, and Nested U-Net for segmentation, followed by a 3D RCNN for quantitative measurements. The approach demonstrates high accuracy across kidneys, liver, spleen, and prostate, achieving precision and recall >95% and AUCs above 0.95 with low MSE, indicating robust consistency with ground truth. A large, diverse CT dataset with detailed annotations supports model training and validation, underscoring the method's generalizability to varying anatomy and imaging conditions. The proposed pipeline reduces manual intervention, potentially improving diagnostic efficiency and measurement reliability in clinical radiology, with future work aimed at expanding to additional organs and handling complex pathologies.

Abstract

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.

Figures (3)

• Figure 1: Workflow architecture.
• Figure 2: CT Measurement for each organ
• Figure 3: ROC curve for Measurement values for each organ.