Detecting Anatomical Landmarks From Limited Medical Imaging Data Using Two-Stage Task-Oriented Deep Neural Networks

Zhang, Jun; Liu, Mingxia; Shen, Dinggang

doi:10.1109/tip.2017.2721106

Cited by 159 publications

(84 citation statements)

References 35 publications

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“…• Our proposed method is unique in the sense that we propose a fully automated system with a geodesic map of bones automatically injected into the deep learning settings unlike the state-of-the-art deep approaches where landmarks are annotated in Euclidean space [3], [13]. • While other works learn landmark locations using only spatial location of the landmarks in a digitized image (on the grid) along with context information, we propose to learn the sparsely-spaced landmark relationship on the same bone by utilizing a U-Net based landmark detection algorithm.…”

Section: A Our Contributionmentioning

confidence: 99%

Deep Geodesic Learning for Segmentation and Anatomical Landmarking

Torosdagli

Liberton

Verma

et al. 2019

IEEE Trans. Med. Imaging

107

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In this paper, we propose a novel deep learning framework for anatomy segmentation and automatic landmarking. Specifically, we focus on the challenging problem of mandible segmentation from cone-beam computed tomography (CBCT) scans and identification of 9 anatomical landmarks of the mandible on the geodesic space. The overall approach employs three inter-related steps. In step 1, we propose a deep neural network architecture with carefully designed regularization, and network hyper-parameters to perform image segmentation without the need for data augmentation and complex postprocessing refinement. In step 2, we formulate the landmark localization problem directly on the geodesic space for sparselyspaced anatomical landmarks. In step 3, we propose to use a long short-term memory (LSTM) network to identify closelyspaced landmarks, which is rather difficult to obtain using other standard detection networks. The proposed fully automated method showed superior efficacy compared to the state-of-theart mandible segmentation and landmarking approaches in craniofacial anomalies and diseased states. We used a very challenging CBCT dataset of 50 patients with a high-degree of craniomaxillofacial (CMF) variability that is realistic in clinical practice. Complementary to the quantitative analysis, the qualitative visual inspection was conducted for distinct CBCT scans from 250 patients with high anatomical variability. We have also shown feasibility of the proposed work in an independent dataset from MICCAI Head-Neck Challenge (2015) achieving the state-of-the-art performance. Lastly, we present an in-depth analysis of the proposed deep networks with respect to the choice of hyper-parameters such as pooling and activation functions.

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Section: A Our Contributionmentioning

confidence: 99%

Deep Geodesic Learning for Segmentation and Anatomical Landmarking

Torosdagli

Liberton

Verma

et al. 2019

IEEE Trans. Med. Imaging

107

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“…While some of those methods are proposed for fundamental MR image analysis ( e.g. , anatomical landmark detection (Zhang et al, 2017b)), many approaches focus on the implementation of computer-aided-diagnosis (CAD) systems.…”

Section: Introductionmentioning

confidence: 99%

Landmark-based deep multi-instance learning for brain disease diagnosis

Liu

Zhang

Adeli

et al. 2018

Medical Image Analysis

Self Cite

335

184

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In conventional Magnetic Resonance (MR) image based methods, two stages are often involved to capture brain structural information for disease diagnosis, i.e., 1) manually partitioning each MR image into a number of regions-of-interest (ROIs), and 2) extracting pre-defined features from each ROI for diagnosis with a certain classifier. However, these pre-defined features often limit the performance of the diagnosis, due to challenges in 1) defining the ROIs and 2) extracting effective disease-related features. In this paper, we propose a landmark-based deep multi-instance learning (LDMIL) framework for brain disease diagnosis. Specifically, we first adopt a data-driven learning approach to discover disease-related anatomical landmarks in the brain MR images, along with their nearby image patches. Then, our LDMIL framework learns an end-to-end MR image classifier for capturing both the local structural information conveyed by image patches located by landmarks and the global structural information derived from all detected landmarks. We have evaluated our proposed framework on 1526 subjects from three public datasets (i.e., ADNI-1, ADNI-2, and MIRIAD), and the experimental results show that our framework can achieve superior performance over state-of-the-art approaches.

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“…Different from conventional ROI-based and voxel-based feature representations of MR images, we develop a novel patch-based feature extraction method for computer-aided brain disease diagnosis with MRI data. Specifically, we first identify discriminative anatomical landmarks via group comparison between AD and normal control (NC) subjects, and then learn patch-based feature representations from each landmark location via a deep convolutional neural network (CNN) [24]. The effectiveness of the proposed LDFL method is validated in both tasks of brain disease classification and MR image retrieval.…”

Section: Introductionmentioning

confidence: 99%

Anatomical Landmark Based Deep Feature Representation for MR Images in Brain Disease Diagnosis

Liu

Zhang

Nie

et al. 2018

IEEE J. Biomed. Health Inform.

Self Cite

121

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Most automated techniques for brain disease diagnosis utilize hand-crafted (e.g., voxel-based or region-based) biomarkers from structural magnetic resonance (MR) images as feature representations. However, these hand-crafted features are usually high-dimensional or require regions-of-interest defined by experts. Also, because of possibly heterogeneous property between the hand-crafted features and the subsequent model, existing methods may lead to sub-optimal performances in brain disease diagnosis. In this paper, we propose a landmark-based deep feature learning (LDFL) framework to automatically extract patch-based representation from MRI for automatic diagnosis of Alzheimer’s disease. We first identify discriminative anatomical landmarks from MR images in a data-driven manner, and then propose a convolutional neural network for patch-based deep feature learning. We have evaluated the proposed method on subjects from three public datasets, including the Alzheimer’s disease neuroimaging initiative (ADNI-1), ADNI-2, and the minimal interval resonance imaging in alzheimer’s disease (MIRIAD) dataset. Experimental results of both tasks of brain disease classification and MR image retrieval demonstrate that the proposed LDFL method improves the performance of disease classification and MR image retrieval.

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Detecting Anatomical Landmarks From Limited Medical Imaging Data Using Two-Stage Task-Oriented Deep Neural Networks

Cited by 159 publications

References 35 publications

Deep Geodesic Learning for Segmentation and Anatomical Landmarking

Deep Geodesic Learning for Segmentation and Anatomical Landmarking

Landmark-based deep multi-instance learning for brain disease diagnosis

Anatomical Landmark Based Deep Feature Representation for MR Images in Brain Disease Diagnosis

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