Automatic hippocampus segmentation of 7.0Tesla MR images by combining multiple atlases and auto-context models

Kim, Minjeong; Wu, Guorong; Li, Wei; Wang, Li; Son, Young-Don; Cho, Zang Hee; Shen, Dinggang

doi:10.1016/j.neuroimage.2013.06.006

Cited by 38 publications

(17 citation statements)

References 47 publications

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“…11). Usually, a simple thresholding or level set method [42, 43] can be applied to binarize the likelihood map for segmentation. However, since each voxel in the target image is independently estimated in the multi-atlas segmentation method, the final segmentation could be weird as no shape prior is considered.…”

Section: First Level: Learning Deep Feature Representation and Spmentioning

confidence: 99%

Deformable MR Prostate Segmentation via Deep Feature Learning and Sparse Patch Matching

Guo

Gao

Shen

2016

IEEE Trans. Med. Imaging

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208

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Automatic and reliable segmentation of the prostate is an important but difficult task for various clinical applications such as prostate cancer radiotherapy. The main challenges for accurate MR prostate localization lie in two aspects: (1) inhomogeneous and inconsistent appearance around prostate boundary, and (2) the large shape variation across different patients. To tackle these two problems, we propose a new deformable MR prostate segmentation method by unifying deep feature learning with the sparse patch matching. First, instead of directly using handcrafted features, we propose to learn the latent feature representation from prostate MR images by the stacked sparse auto-encoder (SSAE). Since the deep learning algorithm learns the feature hierarchy from the data, the learned features are often more concise and effective than the handcrafted features in describing the underlying data. To improve the discriminability of learned features, we further refine the feature representation in a supervised fashion. Second, based on the learned features, a sparse patch matching method is proposed to infer a prostate likelihood map by transferring the prostate labels from multiple atlases to the new prostate MR image. Finally, a deformable segmentation is used to integrate a sparse shape model with the prostate likelihood map for achieving the final segmentation. The proposed method has been extensively evaluated on the dataset that contains 66 T2-wighted prostate MR images. Experimental results show that the deep-learned features are more effective than the handcrafted features in guiding MR prostate segmentation. Moreover, our method shows superior performance than other state-of-the-art segmentation methods.

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Section: First Level: Learning Deep Feature Representation and Spmentioning

confidence: 99%

Deformable MR Prostate Segmentation via Deep Feature Learning and Sparse Patch Matching

Guo

Gao

Shen

2016

IEEE Trans. Med. Imaging

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208

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“…Tu et al [6] adopted the probabilistic boosting tree (PBT) for labeling the MR brain images with Haar features and texture features. Also, Kim et al [7] utilized Adaboost algorithm to train classifiers in multiple atlas image spaces. Then, the final segmentation of a target image is achieved by averaging the labeling results from all classifiers.…”

Section: Introductionmentioning

confidence: 99%

“…Tu et al [6] proposed an auto-context model (ACM) to extract the context information embedded in the tentative labeling map of the target image for iterative refinement of labeling results. Kim et al [7] extracted the context information from an initial labeling probability map of the target image, obtained by using the multi-atlas based method. Compared to the multi-atlas based methods, the learning-based methods can easily learn discriminative features and further utilize context information to improve the labeling performance.…”

Section: Introductionmentioning

confidence: 99%

Soft-Split Random Forest for Anatomy Labeling

Gao

Wang

et al. 2015

Machine Learning in Medical Imaging

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Random Forest (RF) has been widely used in the learning-based labeling. In RF, each sample is directed from the root to each leaf based on the decisions made in the interior nodes, also called splitting nodes. The splitting nodes assign a testing sample to either left or right child based on the learned splitting function. The final prediction is determined as the average of label probability distributions stored in all arrived leaf nodes. For ambiguous testing samples, which often lie near the splitting boundaries, the conventional splitting function, also referred to as hard split function, tends to make wrong assignments, hence leading to wrong predictions. To overcome this limitation, we propose a novel soft-split random forest (SSRF) framework to improve the reliability of node splitting and finally the accuracy of classification. Specifically, a soft split function is employed to assign a testing sample into both left and right child nodes with their certain probabilities, which can effectively reduce influence of the wrong node assignment on the prediction accuracy. As a result, each testing sample can arrive at multiple leaf nodes, and their respective results can be fused to obtain the final prediction according to the weights accumulated along the path from the root node to each leaf node. Besides, considering the importance of context information, we also adopt a Haar-features based context model to iteratively refine the classification map. We have comprehensively evaluated our method on two public datasets, respectively, for labeling hippocampus in MR images and also labeling three organs in Head & Neck CT images. Compared with the hard-split RF (HSRF), our method achieved a notable improvement in labeling accuracy.

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“…Automatic segmentation offers reasonable promises, but remains challenging because of noise, limited resolution, and partial volume effect, resulting in weak boundaries of the hippocampus in MR images46. At present, the segmentation accuracy of the hippocampus remains relatively low78910.…”

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confidence: 99%

“…Multiple atlases can be separately registered to the target image to avoid biased registration71019202122232425. The corresponding label of each atlas is warped to the target image space through the deformation field derived from the registration procedure.…”

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confidence: 99%

Hippocampus Segmentation Based on Local Linear Mapping

Pang

Jiang

et al. 2017

Sci Rep

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We propose local linear mapping (LLM), a novel fusion framework for distance field (DF) to perform automatic hippocampus segmentation. A k-means cluster method is propose for constructing magnetic resonance (MR) and DF dictionaries. In LLM, we assume that the MR and DF samples are located on two nonlinear manifolds and the mapping from the MR manifold to the DF manifold is differentiable and locally linear. We combine the MR dictionary using local linear representation to present the test sample, and combine the DF dictionary using the corresponding coefficients derived from local linear representation procedure to predict the DF of the test sample. We then merge the overlapped predicted DF patch to obtain the DF value of each point in the test image via a confidence-based weighted average method. This approach enabled us to estimate the label of the test image according to the predicted DF. The proposed method was evaluated on brain images of 35 subjects obtained from SATA dataset. Results indicate the effectiveness of the proposed method, which yields mean Dice similarity coefficients of 0.8697, 0.8770 and 0.8734 for the left, right and bi-lateral hippocampus, respectively.

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Automatic hippocampus segmentation of 7.0Tesla MR images by combining multiple atlases and auto-context models

Cited by 38 publications

References 47 publications

Deformable MR Prostate Segmentation via Deep Feature Learning and Sparse Patch Matching

Deformable MR Prostate Segmentation via Deep Feature Learning and Sparse Patch Matching

Soft-Split Random Forest for Anatomy Labeling

Hippocampus Segmentation Based on Local Linear Mapping

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