A Learning-Based Method to Improve Pelvis Cone Beam CT Image Quality for Prostate Cancer Radiation Therapy

Yang, Xiaofeng; Wang, T.; Lei, Yang; Jiang, Xiaojun; Jani, Ashesh B.; Patel, Pretesh; Tang, Xiangyang; Dhabaan, Anees; Liu, T.

doi:10.1016/j.ijrobp.2018.07.1124

Cited by 3 publications

(3 citation statements)

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“…Inspired by the success of DL in computer vision, researchers have proposed various methods to extend the use of DL techniques to medical imaging. To date, DL has been extensively studied in medical image segmentation , image synthesis , image enhancement and correction [97][98][99][100][101][102][103][104][105][106][107], and registration [108][109][110][111][112][113][114][115][116][117][118][119][120][121]. DL-based multi-organ segmentation technique represents a significant potential in daily practices of radiation therapy since it can expedite the contouring process, improve contour accuracy and consistency and promote compliance to delineation guidelines [39,45,[122][123][124][125][126].…”

Section: Introductionmentioning

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: Introductionmentioning

confidence: 99%

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

et al. 2021

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“…Based on different goals, 3D and 2D medical images are usually the datasets. Depending on the network design and graphics processing unit (GPU) memory limitation, some methods directly use the whole volume as input to train the network [91], while some methods process the 3D image slice by slice, called as 2.5D [54], rest works used 2D/3D patches [2,23,59,64]. The 3D-based approaches take 3D patches or whole volume as input and utilize 3D convolution kernels to extract spatial and contextual information from the input images.…”

Section: Network Input Dimension and Sizementioning

confidence: 99%

Generative Adversarial Network for Image Synthesis

Lei¹,

Qiu²,

Wang³

et al. 2020

Preprint

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This chapter reviews recent developments of generative adversarial networks (GAN)-based methods for medical and biomedical image synthesis tasks. These methods are classified into conditional GAN and Cycle-GAN according to the network architecture designs. For each category, a literature survey is given, which covers discussions of the network architecture designs, highlights important contributions and identifies specific challenges.

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“…Inspired by the success of DL in computer vision, researchers have attempted to extend the DL-based techniques to medical imaging. DL-based methods have been extensively explored in medical imaging for the purposes of segmentation [19,21,72,65,66,70,71,74,77,172,140,141,142,146,147,150,153,159,148], synthesis [22,67,73,76,86,87,88,119,120,149,169,167,170], enhancement and correction [20,39,151,152,145,148,168,174,175], and registration [26,120,72,169,172]. DL-based multi-organ segmentation techniques represent a significant innovation in daily practices of radiation therapy, expediting the segmentation process, enhancing contour consistency and promoting compliance to delineation guidelines…”

Section: Introductionmentioning

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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A Learning-Based Method to Improve Pelvis Cone Beam CT Image Quality for Prostate Cancer Radiation Therapy

Cited by 3 publications

References 0 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

Generative Adversarial Network for Image Synthesis

Deep Learning in Multi-organ Segmentation

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