Classification of Pancreatic Cysts in Computed Tomography Images Using a Random Forest and Convolutional Neural Network Ensemble

Dmitriev, Konstantin; Kaufman, Arie; Javed, Ammar A.; Hruban, Ralph H.; Fishman, Elliot K.; Lennon, Anne Marie; Saltz, Joel

doi:10.1007/978-3-319-66179-7_18

Cited by 46 publications

(42 citation statements)

References 13 publications

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“…The use of radiomics also appears to outperform conventional clinical and radiologic methods in discriminating between cyst types. In a study of 134 patients with a variety of histologically-confirmed PCLs, Dmitriev et al found that manually selected quantitative features (location, intensity, and shape) in preoperative CTs more accurately classified cystic lesions than conventional clinical criteria (accuracy 80% vs. 62%) [ 40 ]. This study also found that the use of convolutional neural network features performed similarly to manual quantitative features, but the combination of both reached the highest accuracy (84%).…”

Section: Radiomics To Identify Cyst Typementioning

confidence: 99%

Radiomics for the Diagnosis and Differentiation of Pancreatic Cystic Lesions

2020

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Radiomics, also known as quantitative imaging or texture analysis, involves extracting a large number of features traditionally unmeasured in conventional radiological cross-sectional images and converting them into mathematical models. This review describes this approach and its use in the evaluation of pancreatic cystic lesions (PCLs). This discipline has the potential of more accurately assessing, classifying, risk stratifying, and guiding the management of PCLs. Existing studies have provided important insight into the role of radiomics in managing PCLs. Although these studies are limited by the use of retrospective design, single center data, and small sample sizes, radiomic features in combination with clinical data appear to be superior to the current standard of care in differentiating cyst type and in identifying mucinous PCLs with high-grade dysplasia. Combining radiomic features with other novel endoscopic diagnostics, including cyst fluid molecular analysis and confocal endomicroscopy, can potentially optimize the predictive accuracy of these models. There is a need for multicenter prospective studies to elucidate the role of radiomics in the management of PCLs.

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Section: Radiomics To Identify Cyst Typementioning

confidence: 99%

Radiomics for the Diagnosis and Differentiation of Pancreatic Cystic Lesions

2020

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“…Bottleneck layer (1×1 convolutional layer) before each 3×3 convolution was used to reduce the number of input feature-maps, and thus to improve computational efficiency. For comparison with traditional CNN, we designed a similar architecture as the model in [5] which specifically tailoring to pancreatic cysts classification.…”

Section: Implementation Detailsmentioning

confidence: 99%

Differential Diagnosis for Pancreatic Cysts in CT Scans Using Densely-Connected Convolutional Networks

Reichert

Lin

et al. 2019

2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

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The lethal nature of pancreatic ductal adenocarcinoma (PDAC) calls for early differential diagnosis of pancreatic cysts, which are identified in up to 16% of normal subjects, and some of which may develop into PDAC. Previous computer-aided developments have achieved certain accuracy for classification on segmented cystic lesions in CT. However, pancreatic cysts have a large variation in size and shape, and the precise segmentation of them remains rather challenging, which restricts the computer-aided interpretation of CT images acquired for differential diagnosis. We propose a computer-aided framework for early differential diagnosis of pancreatic cysts without pre-segmenting the lesions using densely-connected convolutional networks (Dense-Net). The Dense-Net learns high-level features from whole abnormal pancreas and builds mappings between medical imaging appearance to different pathological types of pancreatic cysts. To enhance the clinical applicability, we integrate saliency maps in the framework to assist the physicians to understand the decision of the deep learning method. The test on a cohort of 206 patients with 4 pathologically confirmed subtypes of pancreatic cysts has achieved an overall accuracy of 72.8%, which is significantly higher than the baseline accuracy of 48.1%, which strongly supports the clinical potential of our developed method.

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“…Deep learning approaches such as convolutional neural networks (CNNs) have achieved superior performance in various fields such as classification, detection, segmentation, and super-resolution in images [5][6][7][8][9][10][11]. CNNs can automatically extract multilevel features, specific to the application, from images.…”

Section: Introductionmentioning

confidence: 99%

Improving Image Resolution of Whole-Heart Coronary MRA Using Convolutional Neural Network

et al. 2019

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Whole-heart coronary magnetic resonance angiography (WHCMRA) permits the noninvasive assessment of coronary artery disease without radiation exposure. However, the image resolution of WHCMRA is limited. Recently, convolutional neural networks (CNNs) have obtained increased interest as a method for improving the resolution of medical images. The purpose of this study is to improve the resolution of WHCMRA images using a CNN. Free-breathing WHCMRA images with 512 × 512 pixels (pixel size = 0.65 mm) were acquired in 80 patients with known or suspected coronary artery disease using a 1.5 T magnetic resonance (MR) system with 32 channel coils. A CNN model was optimized by evaluating CNNs with different structures. The proposed CNN model was trained based on the relationship of signal patterns between low-resolution patches (small regions) and the corresponding high-resolution patches using a training dataset collected from 40 patients. Images with 512 × 512 pixels were restored from 256 × 256 down-sampled WHCMRA images (pixel size = 1.3 mm) with three different approaches: the proposed CNN, bicubic interpolation (BCI), and the previously reported super-resolution CNN (SRCNN). Highresolution WHCMRA images obtained using the proposed CNN model were significantly better than those of BCI and SRCNN in terms of root mean squared error, peak signal to noise ratio, and structure similarity index measure with respect to the original WHCMRA images. The proposed CNN approach can provide high-resolution WHCMRA images with better accuracy than BCI and SRCNN. The high-resolution WHCMRA obtained using the proposed CNN model will be useful for identifying coronary artery disease.

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Classification of Pancreatic Cysts in Computed Tomography Images Using a Random Forest and Convolutional Neural Network Ensemble

Cited by 46 publications

References 13 publications

Radiomics for the Diagnosis and Differentiation of Pancreatic Cystic Lesions

Radiomics for the Diagnosis and Differentiation of Pancreatic Cystic Lesions

Differential Diagnosis for Pancreatic Cysts in CT Scans Using Densely-Connected Convolutional Networks

Improving Image Resolution of Whole-Heart Coronary MRA Using Convolutional Neural Network

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