Su1337 - Deep Learning to Diagnose Intraductal Papillary Mucinous Neoplasms (IPMN) with MRI

Corral, Juan E.; Hussein, Sarfaraz; Kandel, Pujan; Bolan, Candice W.; Wallace, Michael B.; Bagci, Ulas

doi:10.1016/s0016-5085(18)31973-5

Cited by 4 publications

(3 citation statements)

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“…FCN-like networks were then applied to medical image segmentation, the proposed U-Net [22] obtained performance in neuronal structure and cell segmentation that matched conventional methods. Increasing numbers of FCNbased methods have been proposed, and some have gained significant success in different medical image segmentation problems [23][24][25][26]. For example, the two cascaded FCN, i.e., W-Net, has been applied to PET/CT images with 68 Ga-Pentixafor for automatic detection and segmentation of multiple myeloma lesions in bone [27].…”

Section: Introductionmentioning

confidence: 99%

Deep neural network for automatic characterization of lesions on 68Ga-PSMA-11 PET/CT

Zhao

Gafita

Vollnberg

et al. 2019

Eur J Nucl Med Mol Imaging

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Purpose This study proposes an automated prostate cancer (PC) lesion characterization method based on the deep neural network to determine tumor burden on 68 Ga-PSMA-11 PET/CT to potentially facilitate the optimization of PSMA-directed radionuclide therapy. Methods We collected 68 Ga-PSMA-11 PET/CT images from 193 patients with metastatic PC at three medical centers. For proofof-concept, we focused on the detection of pelvis bone and lymph node lesions. A deep neural network (triple-combining 2.5D U-Net) was developed for the automated characterization of these lesions. The proposed method simultaneously extracts features from axial, coronal, and sagittal planes, which mimics the workflow of physicians and reduces computational and memory requirements. Results Among all the labeled lesions, the network achieved 99% precision, 99% recall, and an F1 score of 99% on bone lesion detection and 94%, precision 89% recall, and an F1 score of 92% on lymph node lesion detection. The segmentation accuracy is lower than the detection. The performance of the network was correlated with the amount of training data. Conclusion We developed a deep neural network to characterize automatically the PC lesions on 68 Ga-PSMA-11 PET/CT. The preliminary test within the pelvic area confirms the potential of deep learning methods. Increasing the amount of training data should further enhance the performance of the proposed method and may ultimately allow whole-body assessments.

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Section: Introductionmentioning

confidence: 99%

Deep neural network for automatic characterization of lesions on 68Ga-PSMA-11 PET/CT

Zhao

Gafita

Vollnberg

et al. 2019

Eur J Nucl Med Mol Imaging

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“…[23] These techniques are being increasingly applied to medical imaging to assist radiologists and pathologists. [24] In gastroenterology, the previous studies have already used deep neural networks to classify colorectal polyps on biopsy and colonoscopy images,[252627] intraductal papillary mucinous neoplasms in magnetic resonance images,[28] and diabetic retinopathy in retinal fundus photographs. [29] For CD in particular, large video datasets captured during endoscopies have facilitated quantitative analysis with deep learning.…”

Section: Introductionmentioning

confidence: 99%

Automated Detection of Celiac Disease on Duodenal Biopsy Slides: A Deep Learning Approach

Wei

Jackson

et al. 2019

Journal of Pathology Informatics

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Context: Celiac disease (CD) prevalence and diagnosis have increased substantially in recent years. The current gold standard for CD confirmation is visual examination of duodenal mucosal biopsies. An accurate computer-aided biopsy analysis system using deep learning can help pathologists diagnose CD more efficiently. Subjects and Methods: In this study, we trained a deep learning model to detect CD on duodenal biopsy images. Our model uses a state-of-the-art residual convolutional neural network to evaluate patches of duodenal tissue and then aggregates those predictions for whole-slide classification. We tested the model on an independent set of 212 images and evaluated its classification results against reference standards established by pathologists. Results: Our model identified CD, normal tissue, and nonspecific duodenitis with accuracies of 95.3%, 91.0%, and 89.2%, respectively. The area under the receiver operating characteristic curve was >0.95 for all classes. Conclusions: We have developed an automated biopsy analysis system that achieves high performance in detecting CD on biopsy slides. Our system can highlight areas of interest and provide preliminary classification of duodenal biopsies before review by pathologists. This technology has great potential for improving the accuracy and efficiency of CD diagnosis.

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“…These techniques are being increasingly applied to medical imaging to assist radiologists and pathologists [25]. In gastroenterology, previous studies have already used deep neural networks to classify colorectal polyps on biopsy and colonoscopy images [26][27][28], intraductal papillary mucinous neoplasms in MRI images [29], and diabetic retinopathy in retinal fundus photographs [30]. For CD in particular, large video datasets captured during endoscopies have facilitated quantitative analysis with deep learning [31,32].…”

Section: Introductionmentioning

confidence: 99%

Automated detection of celiac disease on duodenal biopsy slides: a deep learning approach

Wei¹,

Wei²,

Jackson³

et al. 2019

Preprint

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Celiac disease prevalence and diagnosis have increased substantially in recent years. The current gold standard for celiac disease confirmation is visual examination of duodenal mucosal biopsies. An accurate computer-aided biopsy analysis system using deep learning can help pathologists diagnose celiac disease more efficiently. In this study, we trained a deep learning model to detect celiac disease on duodenal biopsy images. Our model uses a state-of-the-art residual convolutional neural network to evaluate patches of duodenal tissue and then aggregates those predictions for whole-slide classification. We tested the model on an independent set of 212 images and evaluated its classification results against reference standards established by pathologists. Our model identified celiac disease, normal tissue, and nonspecific duodenitis with accuracies of 95.3%, 91.0%, and 89.2%, respectively. The area under the receiver operating characteristic curve was greater than 0.95 for all classes. We have developed an automated biopsy analysis system that achieves high performance in detecting celiac disease on biopsy slides. Our system can highlight areas of interest and provide preliminary classification of duodenal biopsies prior to review by pathologists. This technology has great potential for improving the accuracy and efficiency of celiac disease diagnosis.

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Su1337 - Deep Learning to Diagnose Intraductal Papillary Mucinous Neoplasms (IPMN) with MRI

Cited by 4 publications

References 0 publications

Deep neural network for automatic characterization of lesions on 68Ga-PSMA-11 PET/CT

Deep neural network for automatic characterization of lesions on 68Ga-PSMA-11 PET/CT

Automated Detection of Celiac Disease on Duodenal Biopsy Slides: A Deep Learning Approach

Automated detection of celiac disease on duodenal biopsy slides: a deep learning approach

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