An Artificial Agent for Anatomical Landmark Detection in Medical Images

Ghesu, Florin C.; Georgescu, Bogdan; Mansi, Tommaso; Neumann, Dominik; Hornegger, Joachim; Comanicìu, Dorin

doi:10.1007/978-3-319-46726-9_27

Cited by 116 publications

(88 citation statements)

References 6 publications

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“…They used landmark maps, where each landmark is represented by a Gaussian, as ground truth input data and the network is directly trained to predict this landmark map. Another interesting approach was published by Ghesu et al (2016a), in which reinforcement learning is applied to the identification of landmarks. The authors showed promising results in several tasks: 2D cardiac MRI and ultrasound (US) and 3D head/neck CT.…”

Section: Detection 321 Organ Region and Landmark Localizationmentioning

confidence: 99%

A survey on deep learning in medical image analysis

Litjens

Kooi

Bejnordi

et al. 2017

Medical Image Analysis

9,051

4,996

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Deep learning algorithms, in particular convolutional networks, have rapidly become a methodology of choice for analyzing medical images. This paper reviews the major deep learning concepts pertinent to medical image analysis and summarizes over 300 contributions to the field, most of which appeared in the last year. We survey the use of deep learning for image classification, object detection, segmentation, registration, and other tasks. Concise overviews are provided of studies per application area: neuro, retinal, pulmonary, digital pathology, breast, cardiac, abdominal, musculoskeletal. We end with a summary of the current state-of-the-art, a critical discussion of open challenges and directions for future research.

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Section: Detection 321 Organ Region and Landmark Localizationmentioning

confidence: 99%

A survey on deep learning in medical image analysis

Litjens

Kooi

Bejnordi

et al. 2017

Medical Image Analysis

9,051

4,996

View full text Add to dashboard Cite

show abstract

“…, with millions of images [1], [2], [3]. Although several deep learning based landmark detection methods have been proposed in medical image analysis [4], [5], [6], [7], it is still challenging to detect anatomical landmarks for medical images, due to limited training data at hand. Also, the total number of weights to be learned in deep neural networks for 3D medical images is much larger than that for 2D natural images.…”

Section: Introductionmentioning

confidence: 99%

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

Zhang

Liu

Shen

2017

IEEE Trans. on Image Process.

158

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One of the major challenges in anatomical landmark detection, based on deep neural networks, is the limited availability of medical imaging data for network learning. To address this problem, we present a two-stage task-oriented deep learning (T2DL) method to detect large-scale anatomical landmarks simultaneously in real time, using limited training data. Specifically, our method consists of two deep convolutional neural networks (CNN), with each focusing on one specific task. Specifically, to alleviate the problem of limited training data, in the first stage, we propose a CNN based regression model using millions of image patches as input, aiming to learn inherent associations between local image patches and target anatomical landmarks. To further model the correlations among image patches, in the second stage, we develop another CNN model, which includes a) a fully convolutional network (FCN) that shares the same architecture and network weights as the CNN used in the first stage and also b) several extra layers to jointly predict coordinates of multiple anatomical landmarks. Importantly, our method can jointly detect large-scale (e.g., thousands of) landmarks in real time. We have conducted various experiments for detecting 1200 brain landmarks from the 3D T1-weighted magnetic resonance (MR) images of 700 subjects, and also 7 prostate landmarks from the 3D computed tomography (CT) images of 73 subjects. The experimental results show the effectiveness of our method regarding both accuracy and efficiency in the anatomical landmark detection.

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“…With a well-qualified exploration space, the DQN is promised to capture the prior about pancreas location even with limited data. Previous work [24]- [26] has proposed DQN for efficient object detection, and recent work [27], [28] applied it to anatomical landmark and breast lesion detection, suggesting its competence in MIA. Nonetheless, the incorporation of DQN into more complex Medical Image Segmentation problems has never been expolred, which is the focus of our work.…”

Section: Object Detectionmentioning

confidence: 99%

Deep Q Learning Driven CT Pancreas Segmentation With Geometry-Aware U-Net

Man

Huang

Feng

et al. 2019

IEEE Trans. Med. Imaging

119

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Segmentation of pancreas is important for medical image analysis, yet it faces great challenges of class imbalance, background distractions and non-rigid geometrical features. To address these difficulties, we introduce a Deep Q Network(DQN) driven approach with deformable U-Net to accurately segment the pancreas by explicitly interacting with contextual information and extract anisotropic features from pancreas. The DQN based model learns a context-adaptive localization policy to produce a visually tightened and precise localization bounding box of the pancreas. Furthermore, deformable U-Net captures geometryaware information of pancreas by learning geometrically deformable filters for feature extraction. Experiments on NIH dataset validate the effectiveness of the proposed framework in pancreas segmentation.

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An Artificial Agent for Anatomical Landmark Detection in Medical Images

Cited by 116 publications

References 6 publications

A survey on deep learning in medical image analysis

A survey on deep learning in medical image analysis

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

Deep Q Learning Driven CT Pancreas Segmentation With Geometry-Aware U-Net

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