View source for Project Week 25/Segmentation for improving image registration of preoperative MRI with intraoperative ultrasound images for neuro-navigation

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==Key Investigators==
*[http://www.mic.uni-bremen.de/jennifer-nitsch/ Jennifer Nitsch] (University of Bremen, Germany)
*[http://www.mic.uni-bremen.de/cmt-management-team/scheherazade-kras/ Scheherazade Kraß] (University of Bremen, Germany)
* Rackerseder, Julia (Technical University of Munich, Germany)

==Project Description==
{| class="wikitable"
! style="text-align: left; width:27%" |   Objective
! style="text-align: left; width:27%" |   Approach and Plan
! style="text-align: left; width:27%" |   Progress and Next Steps
|- style="vertical-align:top;"
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*Segmented multiple anatomical structures/landmarks in both MRI and Ultrasound (US) images, using machine learning algorithms (applicability of Deep Learning algorithms is currently tested, DL for US-images, data augmentation...).

*The next step would be to analyze the improvement of registration quality with different segmentations/generated landmarks in order to adapt the segmentation algorithms.

*Using advanced segmentation and registration features for iterative robot control. The respective status of the Kuka LWR iiwa (position, configuration) is simulated and visualized as 3-D model in MeVisLab. Medical data sets in-cluding target pose (position and path towards it) can be send and received via the OpenIGTLink network protocol as well.

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<!-- Approach and Plan bullet points -->
*Starting from multi-modal image segmentation in preopertive MRI and intraoperative Ultrasound images,
it would be to discuss in which "form" one or multiple segmented structures should influence the registration result.
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==Illustrations==
[[image:RegistrationInNeuroNavigationSystem.png|500px]]

Using segmented structures as guiding frame for multi-modal image registration:

[[image:MultimodalImageSegmentation3.png|500px]]

LWR Robot simulation in MeVisLab:

[[image:Picture 2016-12-19 13 55 31.png|500px]]  
[[image:Picture 2017-06-26 11 04 59.png|500px]] 

==Background and References==
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In glioma surgery neuronavigation systems assist in determining the tumor's location and estimating its extent.  However, the intraoperative situation diverges seriously from the preoperative situation in the MRI scan
displayed on the navigation system.  The movement of brain tissue during surgery,  i.e.,  caused by brainshift
and tissue removal, must be considered mentally by the surgeon.  A task that gets more challenging in later
phases of the tumor resection. 

Besides, it is an exhaustive issue and the shift of cerebral structures must be
expected being non-uniform and that it implies a deformation of the image data.  This makes it especially hard
to mentally predict and model.

Thus,  intraoperative  imaging  modalities  are  used  to  visualize  the  current  intraoperative  situation.   IUS,
for  instance,  is  easy  to  use  intraoperatively,  offers real-time  information,  is  widely  available  at  low  cost  and
causes no radiation.  These are important advantages when iUS is compared with iCT or iMRI. However, in
image-guided surgery precise image registration of iUS and preMRI and the thereon-based image fusion is still
an unsolved problem.  The different representations of cerebral structures in both modalities as well as
artifacts within the iUS, hinder direct fusion of both modalities.