A fully automated noncontrast CT 3‐D reconstruction algorithm enabled accurate anatomical demonstration for lung segmentectomy

Abstract

Background: Three-dimensional reconstruction of chest computerized tomography (CT) excels in intuitively demonstrating anatomical patterns for pulmonary segmentectomy. However, current methods are labor-intensive and rely on contrast CT. We hereby present a novel fully automated reconstruction algorithm based on noncontrast CT and assess its performance both independently and in combination with surgeons. Methods: A retrospective pilot study was performed. Patients between May 2020 to August 2020 who underwent segmentectomy in our single institution were enrolled. Noncontrast CTs were used for reconstruction. In the first part of the study, the accuracy of the demonstration of anatomical variants by either automated or manual reconstruction algorithm were compared to surgical observation, respectively. In the second part of the study, we tested the accuracy of the identification of anatomical variants by four independent attendees who reviewed 3-D reconstruction in combination with CT scans. Results: A total of 20 cases were enrolled in this study. All segments were represented in this study with two left S1-3, two left S4 + 5, one left S6, five left basal segmentectomies, one right S1, three right S2, 1 right S2b + 3a, one right S3, two right S6 and two right basal segmentectomies. The median time consumption for the automated reconstruction was 280 (205-324) s. Accurate vessel and bronchial detection were achieved in 85% by the AI approach and 80% by Mimics, p = 1.00. The accuracy of vessel classification was 80 and 95% by AI and manual approaches, respectively, p = 0.34. In real-world application, the accuracy of the identification of anatomical variant by thoracic surgeons was 85% by AI+CT, and the median time consumption was 2 (1-3) min. Conclusions: The AI reconstruction algorithm overcame defects of traditional methods and is valuable in surgical planning for segmentectomy. With the AI reconstruction, surgeons may achieve high identification accuracy of anatomical patterns in a short time frame.

Application of three-dimensional computed tomography imaging and reconstructive techniques in lung surgery: A mini-review

“…To date, many surgeons use traditional 2D CT imaging to estimate the resectability of a lesion and decide on operative approach. However, 3D-imaging using CT-based simulation techniques, sometimes assisted by artificial intelligence ( 14 ), can be used to accurately identify individual segments by tracing bronchovascular anatomy ( 15 , 16 ), making it possible to estimate the probability that a cancerous lesion in a single segment has safe anatomical margins for segmentectomy, or needs change of the original surgical plan to another resection (for instance: bi-segmentectomy, lobectomy) if surgical margins are deemed insufficient ( 17 – 21 ). Comparative studies support this by describing a lower percentage of inadequate resection margins when preoperative assessment was conducted using 3D reconstruction instead of conventional 2D-imaging ( 22 , 23 ).…”

Accuracy and efficiency of an artificial intelligence-based pulmonary broncho-vascular three-dimensional reconstruction system supporting thoracic surgery: retrospective and prospective validation study

“…In a previous study, the accuracy of the 3D reconstruction model was verified by comparison with CT, in which the fully automated algorithm reached a stable Dice similarity of 85%. 18 To accurately support clinical practice, a prospective comparison study between AI-based 3D models and anatomical structures is urgently needed. As the AI system reached a reasonably stable Dice similarity coefficient of 89.2%, we verified its accuracy of AI system by comparing the AI-based 3D reconstruction model with intraoperative anatomical structures.…”

AI-based chest CT semantic segmentation algorithm enables semi-automated lung cancer surgery planning by recognizing anatomical variants of pulmonary vessels

“…Based on the automatic 3-D reconstruction system of pulmonary blood vessels and bronchi (InferOperate Thoracic Surgery) ( 23 ), a pulmonary blood vessel semantic segmentation system was developed using deep learning (DL) algorithms for the automatic segmentation and name of the segmental pulmonary arteries and lobular pulmonary veins, aiming to provide guidance for surgery and promote the application of anatomical lobectomy and segmentectomy. In brief, segmental arteries and lobular veins were manually and concisely segmented based on the automatically constructed 3-D blood vessels by senior thoracic surgeons and then used for training the model.…”

“…Two senior thoracic surgeons reviewed these cases back to back, and disagreements would be settled by a third senior thoracic surgeon. To simplify the establishment procedure, possible anatomical variants that represent the spatial pattern of relevant anatomical structures were enumerated in a table, which is inherited from our previous study with minor simplification ( 23 ) and made available to the surgeons ( Supplementary Table 1 ).…”

“…According to literatures, semantic segmentation algorithms are capable of detecting red blood cells for sickle cell disease in microscopic images ( 16 ); deciding the tumor border in pathological images ( 17 , 18 ); recognizing the infection area of coronavirus disease of 2019 (COVID-19) lesions on chest CTs ( 19 ); distinguishing the brachia plexus, fetal head, and lymph node from ultrasound images ( 20 ); segmenting the thalamus, caudate nucleus, and lenticular nucleus in brain MRI ( 21 ); and diagnosing gastrointestinal cancer margins during endoscopy ( 22 ). Aiming to optimize the surgical planning process, we have previously developed a fully automated 3-D reconstruction algorithm ( 23 ) to classify and reconstruct the pulmonary artery and vein. The performance of the algorithm is the same as manual reconstruction.…”