Identifying Periampullary Regions in MRI Images Using Deep Learning

BackgroundDevelopment and validation of a deep learning method to automatically segment the peri-ampullary (PA) region in magnetic resonance imaging (MRI) images.MethodsA group of patients with or without periampullary carcinoma (PAC) was included. The PA regions were manually annotated in MRI images by experts. Patients were randomly divided into one training set, one validation set, and one test set. Deep learning methods were developed to automatically segment the PA region in MRI images. The segmentation performance of the methods was compared in the validation set. The model with the highest intersection over union (IoU) was evaluated in the test set.ResultsThe deep learning algorithm achieved optimal accuracies in the segmentation of the PA regions in both T1 and T2 MRI images. The value of the IoU was 0.68, 0.68, and 0.64 for T1, T2, and combination of T1 and T2 images, respectively.ConclusionsDeep learning algorithm is promising with accuracies of concordance with manual human assessment in segmentation of the PA region in MRI images. This automated non-invasive method helps clinicians to identify and locate the PA region using preoperative MRI scanning.

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Identifying Periampullary Regions in MRI Images Using Deep Learning

10.21203/rs.3.rs-109369/v1 ◽

2020 ◽

Author(s):

Yong Tang ◽

Xinpei Chen ◽

Weijia Wang ◽

Jiali Wu ◽

Yingjun Zheng ◽

...

Keyword(s):

Deep Learning ◽

Learning Algorithm ◽

Learning Method ◽

Training Set ◽

Deep Learning Algorithm ◽

Invasive Method ◽

T1 And T2 ◽

Magnetic Resonance Imaging Mri ◽

Validation Set ◽

Development And Validation

Abstract Background: Development and validation of a deep learning method to automatically segment the peri-ampullary (PA) region in magnetic resonance imaging (MRI) images. Methods: A group of patients with or without periampullary carcinoma (PAC) was included. The PA regions were manually annotated in MRI images by experts. Patients were randomly divided into one training set and one validation set. A deep learning method to automatically segment the PA region in MRI images was developed using the training set. The segmentation performance of the method was evaluated in the validation set. Results: The deep learning algorithm achieved optimal accuracies in the segmentation of the PA regions in both T1 and T2 MRI images. The value of the intersection over union (IoU) was 0.67 and 0.68 for T1 and T2 images, respectively. Conclusions: Deep learning algorithm is promising with accuracies of concordance with manual human assessment in segmentation of the PA region in MRI images. This automated non-invasive method helps clinicians to identify and locate the PA region using preoperative MRI scanning.

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Development and Validation of a Deep Learning Algorithm to Evaluate Endoscopic Disease Activity of Ulcerative Colitis.

Case Medical Research ◽

10.31525/ct1-nct03973437 ◽

2019 ◽

Author(s):

Keyword(s):

Ulcerative Colitis ◽

Deep Learning ◽

Disease Activity ◽

Learning Algorithm ◽

Deep Learning Algorithm ◽

Development And Validation

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Automated Detection and Classification of Desmoplastic Reaction at the Colorectal Tumour Front Using Deep Learning

Cancers ◽

10.3390/cancers13071615 ◽

2021 ◽

Vol 13 (7) ◽

pp. 1615

Author(s):

Ines P. Nearchou ◽

Hideki Ueno ◽

Yoshiki Kajiwara ◽

Kate Lillard ◽

Satsuki Mochizuki ◽

...

Keyword(s):

Deep Learning ◽

Learning Algorithm ◽

Prognostic Significance ◽

Stage Ii ◽

Full Cohort ◽

Test Set ◽

Colorectal Tumour ◽

Desmoplastic Reaction ◽

Deep Learning Algorithm

The categorisation of desmoplastic reaction (DR) present at the colorectal cancer (CRC) invasive front into mature, intermediate or immature type has been previously shown to have high prognostic significance. However, the lack of an objective and reproducible assessment methodology for the assessment of DR has been a major hurdle to its clinical translation. In this study, a deep learning algorithm was trained to automatically classify immature DR on haematoxylin and eosin digitised slides of stage II and III CRC cases (n = 41). When assessing the classifier’s performance on a test set of patient samples (n = 40), a Dice score of 0.87 for the segmentation of myxoid stroma was reported. The classifier was then applied to the full cohort of 528 stage II and III CRC cases, which was then divided into a training (n = 396) and a test set (n = 132). Automatically classed DR was shown to have superior prognostic significance over the manually classed DR in both the training and test cohorts. The findings demonstrated that deep learning algorithms could be applied to assist pathologists in the detection and classification of DR in CRC in an objective, standardised and reproducible manner.

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