Long Cao1, Ruiqiong Shi2, Yangyang Ge3, Lei Xing4, Panli Zuo5, Yan Jia6, Jie Liu7, Yuan He8, Xinhao Wang9, Shaoliang Luan10, Xiangfei Chai11, Wei Guo12. 1. Department of Vascular and Endovascular Surgery, Chinese PLA General Hospital, Beijing, PR China. Electronic address: caolong@301hospital.com.cn. 2. Institute of Information Science, Beijing Jiaotong University, Beijing, PR China; Huiying Medical Technology Co., Ltd., Dongsheng Science and Technology Park, Beijing, PR China. Electronic address: Ruiqiong_Shi@bjtu.edu.cn. 3. Department of Vascular and Endovascular Surgery, Chinese PLA General Hospital, Beijing, PR China. Electronic address: geyangyang@301hospital.com.cn. 4. Department of Radiation Oncology Stanford University School of Medicine, Stanford, CA, USA. Electronic address: lei@stanford.edu. 5. Huiying Medical Technology Co., Ltd., Dongsheng Science and Technology Park, Beijing, PR China. Electronic address: zuopanli@huiyihuiying.com. 6. Huiying Medical Technology Co., Ltd., Dongsheng Science and Technology Park, Beijing, PR China. Electronic address: jiayan@huiyihuiying.com. 7. Department of Vascular and Endovascular Surgery, Chinese PLA General Hospital, Beijing, PR China. Electronic address: liujie3514@163.com. 8. Department of Vascular and Endovascular Surgery, Chinese PLA General Hospital, Beijing, PR China. Electronic address: heyuan@301hospital.com.cn. 9. Department of Vascular and Endovascular Surgery, Chinese PLA General Hospital, Beijing, PR China. Electronic address: wangxinhao@301hospital.com.cn. 10. Department of Vascular and Endovascular Surgery, Chinese PLA General Hospital, Beijing, PR China. Electronic address: luanshaoliang@126.com. 11. Huiying Medical Technology Co., Ltd., Dongsheng Science and Technology Park, Beijing, PR China. Electronic address: chaixiangfei@huiyihuiying.com. 12. Department of Vascular and Endovascular Surgery, Chinese PLA General Hospital, Beijing, PR China. Electronic address: guowei@301hospital.com.cn.
Abstract
PURPOSE: This study sought to establish a robust and fully automated Type B aortic dissection (TBAD) segmentation method by leveraging the emerging deep learning techniques. METHODS: Preoperative CTA images of 276 patients with TBAD were retrospectively collected from January 2011 to December 2018. Using a reproducible manual segmentation protocol of three labels (whole aorta, true lumen (TL), and false lumen (FL)), a ground truth database (n = 276) was established and randomly divided into training and testing sets in a rough 8:1 ratio. Three convolutional neural network (CNN) models were developed on the training set (n = 246): single one-task (CNN1), single multi-task (CNN2), and serial multi-task (CNN3) models. Performance was evaluated using the Dice coefficient score (DCS) and lumen volume accuracy on the testing set (n = 30). Pearson correlation, Intra-class correlation coefficients and Bland-Altman plots were used to evaluate the inter-observer measurement agreement. RESULTS: CNN3 performed the best, with mean DCSs of 0.93 ± 0.01, 0.93 ± 0.01 and 0.91 ± 0.02 for the whole aorta, TL, and FL, respectively (p < 0.05). Each label volume from CNN3 showed excellent agreement with the ground truth, with mean volume differences of -31.05 (-82.76 to 20.65) ml, 4.79 (-11.04 to 20.63) ml, and 8.67(-11.40 to 28.74) ml for the whole aorta, TL, and FL, respectively. The segmentation speed of CNN3 was 0.038 ± 0.006 s/image. CONCLUSION: Deep learning-based model provides a promising approach for accurate and efficient segmentation of TBAD and makes it possible for automated measurements of TBAD anatomical features.
PURPOSE: This study sought to establish a robust and fully automated Type B aortic dissection (TBAD) segmentation method by leveraging the emerging deep learning techniques. METHODS: Preoperative CTA images of 276 patients with TBAD were retrospectively collected from January 2011 to December 2018. Using a reproducible manual segmentation protocol of three labels (whole aorta, true lumen (TL), and false lumen (FL)), a ground truth database (n = 276) was established and randomly divided into training and testing sets in a rough 8:1 ratio. Three convolutional neural network (CNN) models were developed on the training set (n = 246): single one-task (CNN1), single multi-task (CNN2), and serial multi-task (CNN3) models. Performance was evaluated using the Dice coefficient score (DCS) and lumen volume accuracy on the testing set (n = 30). Pearson correlation, Intra-class correlation coefficients and Bland-Altman plots were used to evaluate the inter-observer measurement agreement. RESULTS:CNN3 performed the best, with mean DCSs of 0.93 ± 0.01, 0.93 ± 0.01 and 0.91 ± 0.02 for the whole aorta, TL, and FL, respectively (p < 0.05). Each label volume from CNN3 showed excellent agreement with the ground truth, with mean volume differences of -31.05 (-82.76 to 20.65) ml, 4.79 (-11.04 to 20.63) ml, and 8.67(-11.40 to 28.74) ml for the whole aorta, TL, and FL, respectively. The segmentation speed of CNN3 was 0.038 ± 0.006 s/image. CONCLUSION: Deep learning-based model provides a promising approach for accurate and efficient segmentation of TBAD and makes it possible for automated measurements of TBAD anatomical features.
Authors: Liana D Wobben; Marina Codari; Gabriel Mistelbauer; Antonio Pepe; Kai Higashigaito; Lewis D Hahn; Domenico Mastrodicasa; Valery L Turner; Virginia Hinostroza; Kathrin Baumler; Michael P Fischbein; Dominik Fleischmann; Martin J Willemink Journal: Annu Int Conf IEEE Eng Med Biol Soc Date: 2021-11
Authors: Zahra Sedghi Gamechi; Andres M Arias-Lorza; Zaigham Saghir; Daniel Bos; Marleen de Bruijne Journal: Med Phys Date: 2021-10-29 Impact factor: 4.506
Authors: Kaiyue Diao; Yuntian Chen; Ying Liu; Bo-Jiang Chen; Wan-Jiang Li; Lin Zhang; Ya-Li Qu; Tong Zhang; Yun Zhang; Min Wu; Kang Li; Bin Song Journal: Ann Transl Med Date: 2022-06
Authors: Viacheslav V Danilov; Kirill Yu Klyshnikov; Olga M Gerget; Igor P Skirnevsky; Anton G Kutikhin; Aleksandr A Shilov; Vladimir I Ganyukov; Evgeny A Ovcharenko Journal: Front Cardiovasc Med Date: 2021-07-19