Fully Automatic Volume Measurement of the Spleen at CT Using Deep Learning
Gabriel E. Humpire-Mamani , Joris Bukala, Ernst T. Scholten, Mathias Prokop, Bram van Ginneken, Colin Jacobs
Automatic spleen segmentation using deep learning is feasible in complex scenarios, such as oncologic follow-up, and may aid radiologists in accurately assessing splenic volume change over time.
Purpose: To develop a fully automated algorithm for spleen segmentation and to assess the performance of this algorithm in a large dataset.
Materials and Methods: In this retrospective study, a three-dimensional deep learning network was developed to segment the spleen on thorax-abdomen CT scans. Scans were extracted from patients undergoing oncologic treatment from 2014 to 2017. A total of 1100 scans from 1100 patients were used in this study, and 400 were selected for development of the algorithm. For testing, a dataset of 50 scans was annotated to assess the segmentation accuracy and was compared against the splenic index equation. In a qualitative observer experiment, an enriched set of 100 scan-pairs was used to evaluate whether the algorithm could aid a radiologist in assessing splenic volume change. The reference standard was set by the consensus of two other independent radiologists. A Mann-Whitney U test was conducted to test whether there was a performance difference between the algorithm and the independent observer.
Results: The algorithm and the independent observer obtained comparable Dice scores (P = .834) on the test set of 50 scans of 0.962 and 0.964, respectively. The radiologist had an agreement with the reference standard in 81% (81 of 100) of the cases after a visual classification of volume change, which increased to 92% (92 of 100) when aided by the algorithm.
Conclusion: A segmentation method based on deep learning can accurately segment the spleen on CT scans and may help radiologists to detect abnormal splenic volumes and splenic volume changes.
Read Full Article Here: https://doi.org/10.1148/ryai.2020190102