An Augmentation Strategy for Medical Image Processing Based on Statistical Shape Model and 3D Thin Plate Spline for Deep Learning

Tang, Zhixian; Chen, Kun; Pan, Mingyuan; Wang, Manning; Song, Zhijian

doi:10.1109/access.2019.2941154

Cited by 53 publications

(23 citation statements)

References 27 publications

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“…This model can then be used to generate deformations within the range of plausible parameters. This approach has demonstrated an improvement in segmentation performance 123–127 …”

Section: Methodsmentioning

confidence: 99%

“…This approach has demonstrated an improvement in segmentation performance. [123][124][125][126][127] Other deformable augmentation techniques Javaid et al 37 proposed a methodology for CT segmentation that aimed to simulate intra-and interobserver variability. In addition to basic and elastic deformations, they augmented the contours made on the training data, rather than the images themselves.…”

Section: Statistical Shape Modelsmentioning

confidence: 99%

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A review of medical image data augmentation techniques for deep learning applications

et al. 2021

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Summary Research in artificial intelligence for radiology and radiotherapy has recently become increasingly reliant on the use of deep learning‐based algorithms. While the performance of the models which these algorithms produce can significantly outperform more traditional machine learning methods, they do rely on larger datasets being available for training. To address this issue, data augmentation has become a popular method for increasing the size of a training dataset, particularly in fields where large datasets aren’t typically available, which is often the case when working with medical images. Data augmentation aims to generate additional data which is used to train the model and has been shown to improve performance when validated on a separate unseen dataset. This approach has become commonplace so to help understand the types of data augmentation techniques used in state‐of‐the‐art deep learning models, we conducted a systematic review of the literature where data augmentation was utilised on medical images (limited to CT and MRI) to train a deep learning model. Articles were categorised into basic, deformable, deep learning or other data augmentation techniques. As artificial intelligence models trained using augmented data make their way into the clinic, this review aims to give an insight to these techniques and confidence in the validity of the models produced.

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“…This model can then be used to generate deformations within the range of plausible parameters. This approach has demonstrated an improvement in segmentation performance 123–127 …”

Section: Methodsmentioning

confidence: 99%

Section: Statistical Shape Modelsmentioning

confidence: 99%

A review of medical image data augmentation techniques for deep learning applications

et al. 2021

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show abstract

“…Second, by using a three-dimensional spline to make a simulated image of the simulated shape, the texture has been filled. Finally, it has been used together with real and simulated images to train deep neural networks [5].…”

Section: Related Workmentioning

confidence: 99%

Detection of Leukemia in Microscopic White Blood Cell Images Using Gaussian Feature Convolutional Visual Recognition Algorithm

2020

jcr

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Cancer detection has always been a challenge in the diagnosis and treatment plan for hematological diseases. Manual identification of cancers from microscopic images is necessary. It depends on other factors, including the lack of microscopic images as their knowledge and precise measurement of specific usual or cancer expert classifications. Automated identification of cancer cells from microscopic section images and helps to improve the problem mentioned above, based on biological interpretation. Detection of blood cancer cells by a patient's blood mark microscopic examination using different techniques of image processing. White blood cell cancer images and their essential three-stage there are an enhancement, segmentation, and classification. The stage of image processing to achieve more quality and accuracy in detecting blood cancers. Approaching the conventional segmentation and counting of blood cells is considered as an essential step that helps to extract features to diagnose diseases like leukemia. The image analysis will allow hematologist experts to perform faster and more accurately. In this work, the image recognition problem of White Blood Cells (WBC) cancer is investigated. Different types of white blood cells are classified using a Gaussian Feature Convolutional Visual Recognition (GFCVR).The most important features or segments of these blood cells are provided as input microscopic images to the neural network. The consequences of this analysis are mainly considering the identification of blood cancer affected region with feature extraction for specific white blood cell images. In this proposed approach pre-processing the image, classifies multi-level clustering to find the affected area in blood cells. In this proposed simulation, results in time complexity, accuracy, recall, perception, false classification ratio gives better results compare with other existing methods.

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“…During the last decade, many researchers are inspired by the profound ability of deep learning, which makes better use of contextual information and extracts powerful high-level features. Based on deep learning, a lot of creative and significant researches are conducted in many aspects of medical image analysis, such as X-ray [7], CT [8], PET [9], MRI [10] and histopathological image [11]. Usually, the performance of deep learning method is far superior to that of the traditional modeling method when the number of available samples is large [12].…”

Section: Introductionmentioning

confidence: 99%

Unpaired Stain Transfer Using Pathology-Consistent Constrained Generative Adversarial Networks

Liu

Zhang

Liu

et al. 2021

IEEE Trans. Med. Imaging

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Pathological examination is the gold standard for the diagnosis of cancer. Common pathological examinations include hematoxylin-eosin (H&E) staining and immunohistochemistry (IHC). In some cases, it is hard to make accurate diagnoses of cancer by referring only to H&E staining images. Whereas, the IHC examination can further provide enough evidence for the diagnosis process. Hence, the generation of virtual IHC images from H&Estained images will be a good solution for current IHC examination hard accessibility issue, especially for some low-resource regions. However, existing approaches have limitations in microscopic structural preservation and the consistency of pathology properties. In addition, pixel-level paired data is hard available. In our work, we propose a novel adversarial learning method for effective Ki-67stained image generation from corresponding H&E-stained image. Our method takes fully advantage of structural similarity constraint and skip connection to improve structural details preservation; and pathology consistency constraint and pathological representation network are first proposed to enforce the generated and source images hold the same pathological properties in different staining domains. We empirically demonstrate the effectiveness of our approach on two different unpaired histopathological datasets. Extensive experiments indicate the superior performance of our method that surpasses the state-of-theart approaches by a significant margin. In addition, our approach also achieves a stable and good performance on unbalanced datasets, which shows our method has strong robustness. We believe that our method has significant potential in clinical virtual staining and advance the progress of computer-aided multi-staining histology image analysis.

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An Augmentation Strategy for Medical Image Processing Based on Statistical Shape Model and 3D Thin Plate Spline for Deep Learning

Cited by 53 publications

References 27 publications

A review of medical image data augmentation techniques for deep learning applications

A review of medical image data augmentation techniques for deep learning applications

Detection of Leukemia in Microscopic White Blood Cell Images Using Gaussian Feature Convolutional Visual Recognition Algorithm

Unpaired Stain Transfer Using Pathology-Consistent Constrained Generative Adversarial Networks

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