Deformable Segmentation via Sparse Shape Representation

Abstract

Abstract. Appearance and shape are two key elements exploited in medical image segmentation. However, in some medical image analysis tasks, appearance cues are weak/misleading due to disease/artifacts and often lead to erroneous segmentation. In this paper, a novel deformable model is proposed for robust segmentation in the presence of weak/misleading appearance cues. Owing to the less trustable appearance information, this method focuses on the effective shape modeling with two contributions. First, a shape composition method is designed to incorporate shape prior on-the-fly. Based on two sparsity observations, this method is robust to false appearance information and adaptive to statistically insignificant shape modes. Second, shape priors are modeled and used in a hierarchical fashion. More specifically, by using affinity propagation method, our deformable surface is divided into multiple partitions, on which local shape models are built independently. This scheme facilitates a more compact shape prior modeling and hence a more robust and efficient segmentation. Our deformable model is applied on two very diverse segmentation problems, liver segmentation in PET-CT images and rodent brain segmentation in MR images. Compared to state-of-art methods, our method achieves better performance in both studies.

Automated segmentation of dental CBCT image with prior-guided sequential random forests

“…6 Recently, shape information has been utilized for robust segmentation. [7][8][9][10][11][12] Duy et al, 10 proposed a novel statistical shape model for detection and classification of teeth in CBCTs. However, their method may not be able to handle pathological cases, where shapes often change significantly (as shown by examples in the Sec.…”

Automated compromised right lung segmentation method using a robust atlas-based active volume model with sparse shape composition prior in CT

“…Sparsity has been widely employed for medical image analysis tasks, such as segmentation, registration and reconstruction [6][7][8][9][10][11][12][13][14][15][16]. The main contribution the work on which we report here is threefold: (1) To address the challenge of the original ground truth for the compromised right lung, we generate the robust shape atlas with the refined ground truth from the output of SSC.…”

A Survey on Medical Image Segmentation Methods with Different Modalitites

“…What's more, the proposed calculation can deal with deceiving signs because of inhomogeneous force or foundation mess in the digitized examples. Sparse shape display has appeared to be more powerful than PCAbased shape earlier because of its lack of care to protest impediment [64], [65]. Nonetheless, utilizing all preparation shapes is wasteful amid meager recreation on a substantial dataset at run-time.…”

“…In the dynamic form show, shapes move in view of image appearance data until the point that it achieves a steady state, where the related vitality work accomplishes a base esteem; in the shape derivation arrange, forms advance in view of abnormal state shape preceding oblige the shapes. This elective activity plan of brushing base up and top-down data has been effectively connected to biomedical image division [64], [65].…”