Automatic Pancreas Segmentation Using Coarse-Scaled 2D Model of Deep Learning: Usefulness of Data Augmentation and Deep U-Net

Nishio, Motohiro; Noguchi, Shunjiro; Fujimoto, Koji

doi:10.3390/app10103360

Cited by 34 publications

(21 citation statements)

References 25 publications

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“…If an image has one or more contours associated with it, the same transformation is applied to the contours. Geometric transformations are so common that they were utilised by 92 of the 93 basic augmentation studies 15–106 …”

Section: Methodsmentioning

confidence: 99%

“…Combination is a data augmentation method that generates a new image by combining 2 or more original images. Two studies made use of the ‘mixup’ technique, which works by randomly selecting two images from the dataset and blending the intensities of the corresponding voxels of the two images 60,89,109 . Nishio et al .…”

Section: Methodsmentioning

confidence: 99%

“…Nishio et al . also utilised Random Image Cropping And Patching (RICAP), where, rather than blending the images, four images are randomly selected, cropped and then patched together 89,110 …”

Section: Methodsmentioning

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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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

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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“…According to the study [ 102 ], they used the Cancer Imaging Archive (TCIA) Public Access dataset, consists of 3D CT scans of 512 × 512-pixel resolution from 53 males and 27 female subjects of the 18-76 age group. Liu et al [ 100 ] implemented CNN to the Taiwanese Centre dataset with contrast-enhanced CT images of 370 patients with pdac and 320 controls.…”

Section: Datasetmentioning

confidence: 99%

Pancreatic Cancer Survival Prediction: A Survey of the State-of-the-Art

Bakasa

Viriri

2021

Computational and Mathematical Methods in Medicine

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Cancer early detection increases the chances of survival. Some cancer types, like pancreatic cancer, are challenging to diagnose or detect early, and the stages have a fast progression rate. This paper presents the state-of-the-art techniques used in cancer survival prediction, suggesting how these techniques can be implemented in predicting the overall survival of pancreatic ductal adenocarcinoma cancer (pdac) patients. Because of bewildering and high volumes of data, the recent studies highlight the importance of machine learning (ML) algorithms like support vector machines and convolutional neural networks. Studies predict pancreatic ductal adenocarcinoma cancer (pdac) survival is within the limits of 41.7% at one year, 8.7% at three years, and 1.9% at five years. There is no significant correlation found between the disease stages and the overall survival rate. The implementation of ML algorithms can improve our understanding of cancer progression. ML methods need an appropriate level of validation to be considered in everyday clinical practice. The objective of these techniques is to perform classification, prediction, and estimation. Accurate predictions give pathologists information on the patient’s state, surgical treatment to be done, optimal use of resources, individualized therapy, drugs to prescribe, and better patient management.

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“…For example, a weighted combination of DICE and Binary Cross Entropy loss functions was explored in [29] that improved overall learning capability and segmentation results. Multiple data augmentation strategies, including mixup [34] and RICAP [35] were studied in [36], leading to consistent improvement on pancreas segmentation for various U-Net architectures.…”

Section: A Pancreas Segmentationmentioning

confidence: 99%

MCMC Guided CNN Training and Segmentation for Pancreas Extraction

Mao

et al. 2021

IEEE Access

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Efficient organ segmentation is the precondition of various quantitative analysis. Segmenting the pancreas from abdominal CT images is a challenging task because of its high anatomical variability in shape, size and location. What's more, the pancreas only occupies a small portion in abdomen, and the organ border is very fuzzy. All these factors make the segmentation methods of other organs less suitable for pancreas. In this work, we propose a Markov Chain Monte Carlo (MCMC) guided convolutional neural network (CNN) approach, in order to handle such difficulties in morphological and photometric variabilities. Specifically, the proposed method mainly consists of three steps: First, registration is carried out to mitigate the body weight and location variability. Then, an MCMC scheme is designed to guide the adaptive selection of 3D patches, which are fed to the CNN for training. At the same time, the pancreas distribution is also learned for subsequent segmentation. Eventually, the same MCMC process guides the segmentation process with patch-wise predictions fused using a Bayesian voting scheme. This method is evaluated on the NIH pancreatic dataset including 82 abdominal contrast-enhanced CT volumes. We have achieved a competitive result of 78.13% Dice Similarity Coefficient value and 82.65% Recall value in testing data.

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Automatic Pancreas Segmentation Using Coarse-Scaled 2D Model of Deep Learning: Usefulness of Data Augmentation and Deep U-Net

Cited by 34 publications

References 25 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

Pancreatic Cancer Survival Prediction: A Survey of the State-of-the-Art

MCMC Guided CNN Training and Segmentation for Pancreas Extraction

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