Automatic detection of standard planes in surgical FD-CT volumes

Type: MA thesis

Status: finished

Date: November 1, 2019 - April 30, 2020

Supervisors: Florian Kordon, Felix Denzinger, Andreas Maier, Dr.-Ing. Holger Kunze (Siemens Healthcare GmbH), Markus Weiten (Siemens Healthcare GmbH), Dr. med. Holger Keil (BG Klinik Ludwigshafen)

Thesis Description

Intra-articular fractures are commonly treated by open reduction and internal fixxation (ORIF). This procedure comprises first, reorient the bone fracture into the normal position and secondly fix it using metal screws, plates or rods. Malreduction of the fracture, intra-articular position of the screws, remaining gaps, or steps offs may lead to malunion or post-traumatic osteoarthritis. The use of mobile C-arms to acquire 3D images during intervention has become a standard since it enables the evaluation of fractures of complex anatomical regions. Two dimensional images often lack information about fracture reduction and implant position in non-planar joints [1]. This includes fractures of the tibial head as well as the calcaneus, ankle injuries involving the syndesmosis or spinal injuries among others. After fracture treatment, if the surgeon is not satisfied with the result, a correction can be made within the frame of the intervention and can avoid the patient a revision surgery in the future. Several studies show intraoperative revision rates depending on the anatomical region up to 40% [2].

Acquisition of standard planes that contains key anatomical structures is decisive for the assessment of intervention results. Multiplanar reconstruction (MPR) is the standard method for reconstruction of the 3D image which allows the generation of slices from arbitrary viewpoint and orientation. Absence of information about position between patient and the C-arm device results in the need for adjustment of the standard planes at a workstation in the operating room. Till now, surgeons must manually find standard planes orientation and position which takes from 46 to 55 second depending
on the experience level of the surgeon and can thus be considered a time-consuming and complicated task [3].


No methods for the fully automatic adjustment of standard planes of mobile C-arms have been described. However, it is possible to find several works in other modalities as ultrasound. In [4] a CNN is used to detect transventricular and transcerebrall standard planes in fetal brain ultrasound. The network learns the mapping between a 2D plane, and the transformation required to move the plane towards the standard plane in the volume. Another approach used in [5] is based on reinforcement learning approach to automatically localize transthalamic and transcerebellar standard planes in 3D fetal ultrasound.

This thesis aims to design a framework for the automatic adjustment of standard planes in different anatomical joint regions using deep learning algorithms. The thesis will comprise the following work items:

  • Literature overview of state-of-the-art automatic standard plane adjustment
  • Characterization of standard planes for different anatomical regions
  • Design and formalization of the to be developed method
  • Overview and explanation of the algorithms used
  • Implementation of the plane detection framework
  • Evaluation of results



[1] Paul Alfred Grützner. Rontgenhelfer 3D: Handbuch intraoperative 3D-Bildgebung mit mobilen C-Bögen. Bengelsdorf & Schimmel, 2004.

[2] Jochen Franke, Klaus Wendl, Arnold J Suda, Thomas Giese, Paul Alfred Grützner, and Jan von Recum. Intraoperative three-dimensional imaging in the treatment of calcaneal fractures. JBJS, 96(9):e72, 2014.

[3] Michael Brehler, Joseph Gorres, Jochen Franke, Karl Barth, Sven Y Vetter, Paul A Grützner, Hans-Peter Meinzer, Ivo Wolf, and Diana Nabers. Intra-operative adjustment of standard planes in C-arm CT image data. International journal of computer assisted radiology and surgery, 11(3):495-
504, 2016.

[4] Yuanwei Li, Bishesh Khanal, Benjamin Hou, Amir Alansary, Juan J Cerrolaza, Matthew Sinclair, Jacqueline Matthew, Chandni Gupta, Caroline Knight, Bernhard Kainz, et al. Standard plane detection in 3D fetal ultrasound using an iterative transformation network. In International Conference on Medical Image Computing and Computer-Assisted Intervention, p. 392-400. Springer, 2018.

[5] Haoran Dou, Xin Yang, Jikuan Qian, Wufeng Xue, Hao Qin, Xu Wang, Lequan Yu, Shujun Wang, Yi Xiong, Pheng-Ann Heng, et al. Agent with warm start and active termination for plane localization in 3D ultrasound, 2019.