Automatic Segmentation of Cardiovascular Structures on Chest CT Data Sets: An Update of the TotalSegmentator

Abstract

Introduction

Quantitative analysis is an important factor in radiological routine. Recently the TotalSegmentator was released, a free-to-use segmentation tool with over 104 structures included. Our aim was to add missing and enhance previously included cardiovascular (CV) structures to potentially help find new insights into diseases such as aortic aneurysms in future studies.

The TotalSegmentator data set with 1613 CT scans (mean age 63.6 ± 15.9 (SD); 675 female), was used. CT scans were selected from clinical routine including various protocols and pathologies. The data set was split in training (1472), validation (57) and testing (84). Segmentations were performed in dedicated imaging software using an iterative approach for training to reduce segmentation workload. Eleven structures were added, and segmentations of six structures were enhanced. The Dice similarity score (DICE) and the Normalized surface distance (NSD) were calculated on an internal and external data set. The external validation was performed on the Dongyang data set. The Mann Whitney U test was performed to evaluate the performance increase on the previously included structures.

Results

Median DICE [IQR] and NSD [IQR] were 0.967 [0.020] and 1.000 [0.000], respectively. DICE (p < 0.001) and NSD (p < 0.001) significantly increased for 5/6 structures. On evaluation using the external data set, DICE and NSD were 0.970 [0.020] and 1.000 [0.000].

Conclusion

Accurate segmentations and enhanced segmentations of previously included CV structures were successfully implemented. This suggests further usage in research studies while still running on conventional computers with or without a dedicated graphics processing unit.