An atlas of anatomical variants of subsegmental pulmonary arteries and recognition error analysis

Abstract

Background: Surgery, including lobectomy and segmentectomy, is the major curative intervention for lung cancer. Surgical planning for pulmonary surgery is difficult due to the high variation rate of pulmonary arteries and needs a fine-grained atlas as a reference. We conducted a study to create a surgically oriented atlas and analyzed the error encountered during the production.

Method: A total of 100 Chest CTs performed at Peking University People's Hospital from 2013.09 to 2020.10 were randomly selected for segmental artery labeling. Dicom files were collected for 3D reconstruction. Manual segmentation of each segmental artery was performed by 4 thoracic surgeons. Cross-validation by surgeons was performed to establish the golden standard based on their consensus. Initial recognition errors were recorded accordingly.

Result: The most frequently seen variants for the right upper lobe is 2-branch RA¹+²rec+³ and RA²asc; right middle lobe 2-branch RA⁴a and RA⁴b+⁵; right lower lobe 3-branch RA⁷, RA⁸ and RA⁹+¹⁰; left upper lobe 3-branch LA¹⁺²a+³, LA¹⁺²b, LA¹⁺²c and 1-branch LA⁴+⁵; left lower lobe 2-branch LA⁸ and LA⁹+¹⁰. Top 5 segmental error occurs in RA⁴ (23%), LA⁸ (17%), RA⁹ (17%), RA⁸ (14%) and LA⁹ (11%). A rapid surgical planning tool form was created based on high frequency anatomic variants.

Conclusion: Our research provided an atlas for lobectomy and segmentectomy at the subsegmental or more distal level. We demonstrated that the recognition accuracy of pulmonary arteries in a non-time-sensitive experimental scenario was still unfavorable. We also suggest that extra attention should be paid to certain surgeries during the surgical planning process.

Keywords: 3D reconstruction; artificial intelligence; lung variation; precision surgery; pulmonary artery.