RETRACTED ARTICLE: Medical image semantic segmentation based on deep learning

Jiang, Feng; Grigorev, Aleksey; Rho, Seungmin; Tian, Zhihong; Fu, Yunsheng; Jifara, Worku; Khan, Adil; Liu, Shaohui

doi:10.1007/s00521-017-3158-6

Cited by 112 publications

(47 citation statements)

References 23 publications

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“…Structural correcting adversarial network [40,49] Adversarial training is good for a small number of training images Critic network requires fully connected layer and consumes a lot of parameters Domain adaptation [41,44] Domain adaption is good to enhance segmentation performance…”

Section: Using Features Based On Machine Learning or Deep Learningmentioning

confidence: 99%

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Artificial Intelligence-Based Diagnosis of Cardiac and Related Diseases

Arsalan

Owais

Mahmood

et al. 2020

JCM

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Automatic chest anatomy segmentation plays a key role in computer-aided disease diagnosis, such as for cardiomegaly, pleural effusion, emphysema, and pneumothorax. Among these diseases, cardiomegaly is considered a perilous disease, involving a high risk of sudden cardiac death. It can be diagnosed early by an expert medical practitioner using a chest X-Ray (CXR) analysis. The cardiothoracic ratio (CTR) and transverse cardiac diameter (TCD) are the clinical criteria used to estimate the heart size for diagnosing cardiomegaly. Manual estimation of CTR and other diseases is a time-consuming process and requires significant work by the medical expert. Cardiomegaly and related diseases can be automatically estimated by accurate anatomical semantic segmentation of CXRs using artificial intelligence. Automatic segmentation of the lungs and heart from the CXRs is considered an intensive task owing to inferior quality images and intensity variations using nonideal imaging conditions. Although there are a few deep learning-based techniques for chest anatomy segmentation, most of them only consider single class lung segmentation with deep complex architectures that require a lot of trainable parameters. To address these issues, this study presents two multiclass residual mesh-based CXR segmentation networks, X-RayNet-1 and X-RayNet-2, which are specifically designed to provide fine segmentation performance with a few trainable parameters compared to conventional deep learning schemes. The proposed methods utilize semantic segmentation to support the diagnostic procedure of related diseases. To evaluate X-RayNet-1 and X-RayNet-2, experiments were performed with a publicly available Japanese Society of Radiological Technology (JSRT) dataset for multiclass segmentation of the lungs, heart, and clavicle bones; two other publicly available datasets, Montgomery County (MC) and Shenzhen X-Ray sets (SC), were evaluated for lung segmentation. The experimental results showed that X-RayNet-1 achieved fine performance for all datasets and X-RayNet-2 achieved competitive performance with a 75% parameter reduction.

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Section: Using Features Based On Machine Learning or Deep Learningmentioning

confidence: 99%

“…Jiang et al presented deep convolution neural network-based segmentation using a small amount of data. They used a VGG16 network using prior weight initialization [49]. Table 1 lists the strengths and weaknesses of the existing methods in comparison to X-RayNet for chest anatomy segmentation.…”

Section: Introductionmentioning

confidence: 99%

Artificial Intelligence-Based Diagnosis of Cardiac and Related Diseases

Arsalan

Owais

Mahmood

et al. 2020

JCM

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“…In subjects with extensive parenchymal and/or pleural change it can take approximately one hour to segment the lung volumes and thoracic wall. We plan to fully automate the image segmentation steps using deep-learning methods in the future [41]. The biomarkers will facilitate more accurate and consistent reporting across clinical trials to allow comparisons between RT and drug combination schedules.…”

Section: Discussionmentioning

confidence: 99%

Novel CT-Based Objective Imaging Biomarkers of Long-Term Radiation-Induced Lung Damage

Veiga

Landau

Devaraj

et al. 2018

International Journal of Radiation Oncology*Biology*Physics

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“…The emerging convolutional neural network (CNN), one of a number of deep learning techniques, has contributed to the sphere of medical image processing greatly in recent years [35]- [37]. It benefits from the fact that the end-toend learning of salient feature representations maybe more effective than that of handcrafted features with heuristic tuning parameters [38].…”

Section: Related Workmentioning

confidence: 99%

Fusion of Multi-Size Candidate Regions Enhances Two-Stage Hippocampus Segmentation

et al. 2020

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The hippocampus plays an important role in the memory and cognition abilities of humans. Precise three-dimensional (3D) segmentation of the hippocampus from magnetic resonance imaging scans is of great importance in the diagnosis of neurological diseases. Conventional automatic segmentation methods poorly achieve satisfactory performance because of the irregular shape and small volume of the hippocampus. We propose a novel two-stage segmentation method, which includes a localization stage and a segmentation stage, to handle the task of the 3D segmentation of the hippocampus. In the localization stage, a novel strategy for localizing multi-size candidate regions was developed to improve the sample balance for the 3D segmentation task. In the segmentation stage, a method which fuses the multi-size candidate regions was proposed to improve the accuracy in predicting the hippocampal boundary, after which we aggregated the segmentation results from three orthogonal views to further improve the performance. Quantitative evaluation was performed on the Alzheimer's Disease Neuroimaging Initiative dataset. The experimental results achieved Dice similarity coefficients of 92.48 ± 0.61% and 92.90 ± 0.51% for the left and right hippocampus, respectively, outperforming state-of-the-art studies in hippocampus segmentation tasks.

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RETRACTED ARTICLE: Medical image semantic segmentation based on deep learning

Cited by 112 publications

References 23 publications

Artificial Intelligence-Based Diagnosis of Cardiac and Related Diseases

Artificial Intelligence-Based Diagnosis of Cardiac and Related Diseases

Novel CT-Based Objective Imaging Biomarkers of Long-Term Radiation-Induced Lung Damage

Fusion of Multi-Size Candidate Regions Enhances Two-Stage Hippocampus Segmentation

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