Anatomically constrained deformable 3D reconstruction of intraoperative uterus from preoperative MRI data on uterine fibroid treatment

ABSTRACT

Detections of the uterus using preoperative magnetic resonance imaging (MRI) data are required for intraoperative navigation in High-Intensity Focused Ultrasound (HIFU) treatment of uterine fibroids. The modelling of the intraoperative uterus between empty and full bladder anatomy deems to be a crucial step to quantify uterus deformation caused by the bladder-and-rectum-filling (BRF) technique. In this paper, a novel two-stage conditional generative adversarial network (cGAN) is proposed to perform intraoperative uterus deformable reconstruction with anatomical constraints using only single preoperative MRI data, viz. ACDeformRec. The highly constrained anatomy properties are further captured by a novel attention network that formulates high-level uterus segmentation task by incorporating correlations of surrounding organs for more anatomically plausible reconstruction. The experimental result demonstrates the robustness of ACDeformRec by evaluating its performance on 181 clinical datasets and has achieved the lowest reconstruction error of 0.735 ± 0.045 mm with Dice Similarity Coefficient of 94.23% and Normalised Cross Correlation of 97.23%.

KEYWORDS:

• Anatomical constraints
• conditional generative adversarial network
• HIFU
• uterus deformable reconstruction

Acknowledgments

The authors thank the National Engineering Research Center of Ultrasound Medicine, Chongqing, China, for the clinical dataset and Yingang Wen’s advice on the information of uterus deformation during HIFU surgery.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

This work was supported by the National Natural Science Foundation of China under Grant (82027807, 81771940); and the Beijing Municipal Natural Science Foundation under Grant (7212202).

Notes on contributors

Hee Guan Khor

Hee Guan Khor received his B.S. degree in 2019 in biomedical engineering from Chongqing University, China. He is now a M.E. student in the Department of Biomedical Engineering, School of Medicine, Tsinghua University, Beijing, China. His research interests include medical image processing and intelligent therapy.

Guochen Ning

Guochen Ning received his B.S. degree in 2014 and M.E. degree in 2017 in biomedical engineering from Northeastern University. He is now a Ph.D. student in the Department of Biomedical Engineering, School of Medicine, Tsinghua University, Beijing, China. His research interests include medical image processing and intelligent therapy.

Ting Wang

Ting Wang received her B.S. degree in Biomedical Engineering from Shandong First Medical University in 2010 and her Master’s degree in biomedical Engineering from Chongqing Medical University in 2013. She is now a Ph.D. candidate in the School of Biomedical Engineering at Chongqing Medical University. Her research interest covers medical image processing.

Yingang Wen

Yingang Wen graduated in mechanical manufacturing technology from North University of China in 1997. He worked at Chongqing Haifu Medical Technology Co. Ltd, where his major research focus was on 3D model construction methods and devices, as well as image monitoring methods and devices. He has also worked on US and MR image fusion, as well as surgical navigation system treatment.

Xinran Zhang

Xinran Zhang received a B.S. degree in biomedical engineering from South China University of Technology, Guangzhou, China, in 2013, and a master’s degree from the Department of Biomedical Engineering, School of Medicine, Tsinghua University, Beijing, China, in 2016. She is currently a Research Associate in the Department of Biomedical Engineering, School of Medicine, Tsinghua University, Beijing, China. Her research interests include high-quality and accurate 3D imaging technologies and applications in 3D surgical guidance systems.

Hongen Liao

Hongen Liao, received his Ph.D. degree in biomedical precision engineering from the University of Tokyo, Japan, in 2003. He was a Research Fellow of JSPS. Since 2004, he has been a faculty member at the Graduate School of Engineering, The University of Tokyo, where he became an Associate Professor in 2007. He has been selected as a National Distinguished Professor, China since 2010, and is currently a Full Professor and Vice Dean in the School of Medicine, and the Department of Biomedical Engineering, Tsinghua University, China. His research interests include 3D medical image, image-guided surgery, medical robotics, computer-assisted surgery, and fusion of these innovative healthcare technologies for minimally invasive precision diagnosis and therapy. He is the author and co-author of more than 300 peer-reviewed articles and proceedings papers, as well as 80 international invited lectures, over 50 patents and 320 conference abstracts. He has served as a President of Asian Society for Computer Aided Surgery and Co-chair of Asian-Pacific Activities Working Group, IFMBE.