While deep learning based methods for medical deformable image registration have recently shown significant advances in both speed and accuracy, methods for use in radio therapy are still rarely proposed due to several challenges such as low contrast and artifacts in cone beam CT (CBCT) images or extreme deformations. The aim of image registration in radio therapy is to align a baseline CT and low-dose CBCT images, which allows contours to be propagated and applied doses to be tracked over time. To this end, we present a novel deep learning method for multi-modal deformable CT-CBCT registration. We train a CNN in weakly supervised manner, aiming to optimize an edge-based image similarity and a deformation regularizer including a penalty for local changes of topology and foldings. Additionally, we measure the alignment of given segmentations, facing the problem of extreme deformations. Our method receives only CT and a CBCT images as input and uses groundtruth segmentations exclusively during training. Furthermore, our method is not dependent on the availability of difficult to access ground-truth deformation vector fields. We train and evaluate our method on follow-up image pairs of the pelvis and compare our results to conventional iterative registration algorithms. Our experiments show that the registration accuracy of our deep learning based approach is superior to iterative registration without additional guidance by segmentations and nearly as good as iterative structure guided registration that requires ground-truth segmentations. Furthermore, our deep learning based method runs approximately 100 times faster than the iterative methods.
In navigated liver surgery it is an important task to align intra-operative data to pre-operative planning data.
This work describes a method to register pre-operative 3D-CT-data to tracked intra-operative 2D US-slices.
Instead of reconstructing a 3D-volume out of the two-dimensional US-slice sequence we directly apply the registration
scheme to the 2D-slices. The advantage of this approach is manyfold. We circumvent the time consuming
compounding process, we use only known information, and the complexity of the scheme reduces drastically. As
the liver is a non-rigid organ, we apply non-linear techniques to take care of deformations occurring during the
intervention. During the surgery, computing time is a crucial issue. As the complexity of the scheme is proportional
to the number of acquired slices, we devise a scheme which starts out by selecting a few "key-slices" to
be used in the non-linear registration scheme. This step is followed by multi-level/multi-scale strategies and fast
optimization techniques. In this abstract we briefly describe the new method and show first convincing results.
The resection of a tumor is one of the most common tasks in liver surgery. Here, it is of particular importance to
resect the tumor and a safety margin on the one hand and on the other hand to preserve as much healthy liver
tissue as possible. To this end, a preoperative CT scan is taken in order to come up with a sound resection strategy.
It is the purpose of this paper to compare the preoperative planning with the actual resection result. Obviously
the pre- and postoperative data is not straightforward comparable, a meaningful registration is required. In the
literature one may find a rigid and a landmark-based approach for this task. Whereas the rigid registration does
not compensate for nonlinear deformation the landmark approach may lead to an unwanted overregistration.
Here we propose a fully automatic nonlinear registration with volume constraints which seems to overcome both
aforementioned problems and does lead to satisfactory results in our test cases.
In navigated liver surgery the key challenge is the registration of pre-operative planing and intra-operative
navigation data. Due to the patients individual anatomy the planning is based on segmented, pre-operative
CT scans whereas ultrasound captures the actual intra-operative situation. In this paper we derive a novel
method based on variational image registration methods and additional given anatomic landmarks. For
the first time we embed the landmark information as inequality hard constraints and thereby allowing for
inaccurately placed landmarks. The yielding optimization problem allows to ensure the accuracy of the
landmark fit by simultaneous intensity based image registration. Following the discretize-then-optimize
approach the overall problem is solved by a generalized Gauss-Newton-method. The upcoming linear system
is attacked by the MinRes solver. We demonstrate the applicability of the new approach for clinical data
which lead to convincing results.
The paper is concerned with image registration algorithms for the alignment of computer tomography
(CT) and 3D-ultrasound (US) images of the liver. The necessity of registration arises from the surgeon's
request to benefit from the planning data during surgery. The goal is to align the planning data, derived
from pre-operative CT-images, with the current US-images of the liver acquired during the surgery.
The registration task is complicated by the fact, that the images are of a different modality, that the
US-images are severely corrupted by noise, and that the surgeon is looking for a fast and robust scheme.
To guide and support the registration, additional pairs of corresponding landmarks are prepared. We
will present two different approaches for registration. The first one is based on the pure alignment of
the landmarks using thin plate splines. It has been successfully applied in various applications and is
now transmitted to liver surgery. In the second approach, we mix a volumetric distance measure with
the landmark interpolation constraints. In particular, we investigate the promising normalized gradient
field distance measure. We use data from actual liver surgery to illustrate the applicability and the
characteristics of both approaches. It turns out that both approaches are suitable for the registration
of multi-modal images of the liver.
In image registration of medical data a common and challenging problem is handling intensity-inhomogeneities. These inhomogeneities appear for instance in images of serially sectioned brains caused by the histological staining process or in medical imaging with contrast agents. Beneath this, natural outliers (for instance cells or vessels) produced by the underlying material itself may be mistaken as noise. Both image registration applications have in common that the well known sum of squared differences (SSD) measure would detect false differences. To deal with these kinds of problems, we supplement the common SSD-measure with image derivatives of higher order. Additionally we introduce a non-quadratic penalizer function to the distance measure leading to robust energy. The concepts are well known in optical flow. Overall, we present a variational model which combines all of these properties. This formulation leads to a fast and efficient algorithm. We demonstrate its applicability at the problems described above.