InitRech 2015/2016, sujet 13

Summary

Female pelvic disorder such as the organ prolapse becomes a very common problem as a woman ages.About 20 to 30% women suffer from the severe degree of prolapse and more than 60% of women who are over 60 years are affected by this pathology.These problems are connected to the mobility of female pelvic system.In this system, a meaningful analysis of medical images usually decides the physicain's diagnosis.However, a human perception or a medical experience cannot be avoided and these two factors may cause the variability in the diagnoses.In this paper, people introduced a new method that can be classified as a model-to-image correlation approach to avoid these problems.The method performs fast semi-automatic detection of the bladder,vagina and rectum from magnetic resonance images for geometries reconstruction and further study of the mobilities.

Fisrt of all,people propsed a semi-automatic method to do the detection of the oragns.This step is an important and preliminary step for the further studies and modelling.Then they used a peridodic B-splines and offsets algorithm to create thick surfaces of hollow pelvic organs.This modelling was a step between segmentation and physical modelling.To achieve the surface modelling of patient specifc organ geometries,two steps were needed:segmentation and geometric modelling.People used some algorithms to acomplish these two goals.Despite their success, these methods were all pixel-based approach,which means that they could not provide directely smooth and accurate contour.Hence another modelling method called 'model-to-image' was carried out.

Two main steps were demanded for this method:initialisation of a fine model and deformation of the model.At first,a powerful B-spline functions for buliding the snake model(active contour) were introduced.But because of its demand for good initialisation of models which were not easy to create and for good positions of points,this method could not produce the results that people desired.Thus people introduced a new B-spline-like method.

As model-based approach was widely used for medical image processing,in this paper people introduced a new model-to-image approach.The idea was to combine the method of 'Active Contour' with the 'Virtual Image' approach.People used a cost function to find the best correlation between the virtual images and the real ones.In the end,this method became an energy minimisation problem.

To do the optimisation procedure,four important parts were demanded:

• input data(3D static and 2D dynamic MR images of the patient)
• a mathematical model with variables to be optimised(B-spline Model section)
• a cost function,which links the model to the input data
• an optimiser to minimise the cost function

The database of 19 patients images were gathered to valid this method of segmentation.By the end of each optimisation process,the doctor corrected the shape of organs manually to make sure that the final curves and the contours of organs were well fitted.Then people compared the curves before corrections with the final ones by evaluating error with Dice coefficient,Hausdorff distance and average distance.

To conclude,in this paper,people presented a B-spline-based model-to-image registration approach for segmenting pelvic organs using MR images.It will be more interesting to apply this algorithm for 3D reconstruction of multi-organ despite the performance showed in 2D situation.And it will be more valuable for the purpose of patient-specific medical simulation.

Main Contribution

In the main contribution,authors mainly explained four major steps of the optimisation procedure.They explained how 2D dynamic MR images and 3D static MR images were obtained.They also introduced B-spline geometric model tool by applying mathematical functions.Then they introduced Cost Function that links the model to the input data with images and mathematical formulas.At last,they introduced how an optimiser could find the optimal values of the parameters to minimise the cost function.