LG178: Facial Soft-Tissue Segmentation

Date: 
Friday, February 7, 2014 - 12:00
Speaker: 
Tahir Majeed, PhD
Abstract: 
The importance of the face for socio-ecological interaction is the cause for a high demand on any surgical intervention on the facial musculoskeletal system. Bones and soft-tissues are of major importance for any facial surgical treatment to guarantee an optimal, functional and aesthetical result. For this reason, surgeons want to preoperatively plan, simulate and predict the outcome of the surgery allowing for shorter operation times and improved quality. Accurate simulation requires exact segmentation knowledge of the facial tissues. Thus semi-automatic segmentation techniques are required. In this talk semi-automatic methods for segmentation of the facial soft-tissues, such as muscles, skin and fat, from CT and MRI datasets, using a Markov Random Fields (MRF) framework will be presented. Due to image noise, artifacts and weak edges, it is difficult to segment the object of interest by using image information alone. Segmentations would leak into neighboring structures that have a similar intensity profile. To overcome this problem, additional shape knowledge is incorporated in the energy function which can then be minimized using Graph-Cuts (GC). Incremental approaches by incorporating additional prior shape knowledge are presented. The proposed approaches are not object specific and can be applied to segment any class of objects be that anatomical or non-anatomical from medical or non-medical image datasets, whenever a statistical model is present. In the first approach a 3D mean shape template is used as shape prior, which is integrated into the MRF based energy function. In the second approach, to improve handling of shape variations naturally found in the population, the fixed shape template is replaced by a more robust 3D statistical shape model based on Probabilistic Principal Component Analysis (PPCA). The advantages of using the Probabilistic PCA are that it allows reconstructing the optimal shape and computing the remaining variance of the statistical model from partial information. By using an iterative method, the statistical shape model is then refined using image based cues to get a better fitting of the statistical model to the patient's muscle anatomy. In the third approach, the shape refinement step is further improved by using a non-linear shape update mechanism where vertices of the 3D mesh of the statistical model incur the non-linear penalty depending on the remaining variability of the vertex. The non-linear shape update mechanism provides a more accurate shape update and helps in a finer shape fitting of the statistical model. Finally, a unified approach is presented to segment the relevant facial muscles and the remaining facial soft-tissues (skin and fat). The proposed methods are tested on the challenging example of segmenting the masseter muscle. Qualitative and quantitative experimental results show that by incorporating prior shape knowledge leaking can be effectively constrained to obtain better segmentation results.