Automatic Vertebrae Localization and Identification by Combining Deep SSAE Contextual Features and Structured Regression Forest

Wang, Xuchu; Zhai, Suiqiang; Niu, Yanmin

doi:10.1007/s10278-018-0140-5

Cited by 18 publications

(7 citation statements)

References 28 publications

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“…DSAE has been shown to have high classification accuracy and speed for liver segmentation on CT images [139]. AE can learn medical image deep contextual features from large-range input samples to improve their contextual discrimination ability [142]. Validated on the 98 spine CT scans from the public MICCAI CSI 2014 dataset, the SSAE method could effectively and automatically locate and identify spinal targets in CT scans, and achieve high localization accuracy without making any assumptions about visual field in CT scans [140].…”

Section: Auto-encodermentioning

confidence: 99%

A review of deep learning based methods for medical image multi-organ segmentation

et al. 2021

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Deep learning has revolutionized image processing and achieved the-state-of-art performance in many medical image segmentation tasks. Many deep learning-based methods have been published to segment different parts of the body for different medical applications. It is necessary to summarize the current state of development for deep learning in the field of medical image segmentation. In this paper, we aim to provide a comprehensive review with a focus on multi-organ image segmentation, which is crucial for radiotherapy where the tumor and organs-at-risk need to be contoured for treatment planning. We grouped the surveyed methods into two broad categories which are 'pixel-wise classification' and 'end-to-end segmentation'. Each category was divided into subgroups according to their network design. For each type, we listed the surveyed works, highlighted important contributions and identified specific challenges. Following the detailed review, we discussed the achievements, shortcomings and future potentials of each category. To enable direct comparison, we listed the performance of the surveyed works that used thoracic and head-and-neck benchmark datasets.

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Section: Auto-encodermentioning

confidence: 99%

A review of deep learning based methods for medical image multi-organ segmentation

et al. 2021

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“…CSDAE can potentially save the radiologists time and effort, allowing them to focus on higher-level risk CXRs. In contrast to machine learning, whose performance depends on hand-craft features, AE can learn medical image deep contextual features instead of hand-crafted ones by building larger-range input samples to improve their contextual discrimination ability [155]. Validated on public MICCAI CS2014 dataset which includes a challenging dataset of 98 spine CT scans, the SSAE method could effectively and automatically locate and identify spinal targets in CT scans, and achieve higher localization accuracy, low model complexity without making any assumptions about visual field in CT scans [102].…”

Section: Discussionmentioning

confidence: 99%

“…To improve the discriminability of these features, a further refinement using a supervised fashion and fine-tuning was integrated. Similarly, Wang et al proposed to localize and identify vertebrae by combining SSAE contextual features and structured regression forest (SRF) [155]. Contextual features were extracted via SSAE in an unsupervised way, and were then fed into SRF to achieve whole spine localization.…”

Section: Overview Of Workmentioning

confidence: 99%

Deep Learning in Multi-organ Segmentation

Yang¹,

Fu²,

Wang³

et al. 2020

Preprint

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This paper presents a review of deep learning (DL) in multi-organ segmentation. We summarized the latest DL-based methods for medical image segmentation and their applications. These methods were classified into six categories according to their network design. For each category, we listed the surveyed works, highlighted important contributions and identified specific challenges. Following the detailed review of each category, we briefly discussed its achievements, shortcomings and future potentials. We provided a comprehensive comparison among DL-based methods for thoracic and head & neck multi-organ segmentation using benchmark datasets, including the 2017 AAPM Thoracic Autosegmentation Challenge datasets and 2015 MICCAI Head Neck Auto-Segmentation Challenge datasets.

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“…Qualitative analysis of these markers shows predictive value in the task of detecting healthy, early, and late age-related muscular degeneration. A combination of contextual features from deep stacked sparse AE (SSAE) and structured regression forest for vertebrae localization and identification was created to overcome handcrafted and low-level features of spine structure [126]. The method employs SSAE to learn deep contextual features by building larger-range input samples to improve the contextual discrimination ability.…”

Section: Review Of Deep Learning Implementation In Health Carementioning

confidence: 99%

Deep Learning Intervention for Health Care Challenges: Some Biomedical Domain Considerations

Igbe

Liu

et al. 2019

JMIR Mhealth Uhealth

145

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The use of deep learning (DL) for the analysis and diagnosis of biomedical and health care problems has received unprecedented attention in the last decade. The technique has recorded a number of achievements for unearthing meaningful features and accomplishing tasks that were hitherto difficult to solve by other methods and human experts. Currently, biological and medical devices, treatment, and applications are capable of generating large volumes of data in the form of images, sounds, text, graphs, and signals creating the concept of big data. The innovation of DL is a developing trend in the wake of big data for data representation and analysis. DL is a type of machine learning algorithm that has deeper (or more) hidden layers of similar function cascaded into the network and has the capability to make meaning from medical big data. Current transformation drivers to achieve personalized health care delivery will be possible with the use of mobile health (mHealth). DL can provide the analysis for the deluge of data generated from mHealth apps. This paper reviews the fundamentals of DL methods and presents a general view of the trends in DL by capturing literature from PubMed and the Institute of Electrical and Electronics Engineers database publications that implement different variants of DL. We highlight the implementation of DL in health care, which we categorize into biological system, electronic health record, medical image, and physiological signals. In addition, we discuss some inherent challenges of DL affecting biomedical and health domain, as well as prospective research directions that focus on improving health management by promoting the application of physiological signals and modern internet technology.

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Automatic Vertebrae Localization and Identification by Combining Deep SSAE Contextual Features and Structured Regression Forest

Cited by 18 publications

References 28 publications

A review of deep learning based methods for medical image multi-organ segmentation

A review of deep learning based methods for medical image multi-organ segmentation

Deep Learning in Multi-organ Segmentation

Deep Learning Intervention for Health Care Challenges: Some Biomedical Domain Considerations

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