Mr. Matthew Phelan Profile

Matthew Phelan

at Duke Univ

SPIE Involvement:

Author

Publications (2)

This will count as one of your downloads.

You will have access to both the presentation and article (if available).

DOWNLOAD NOW

This content is available for download via your institution's subscription. To access this item, please sign in to your personal account.

Email or Username Forgot your username?

Password Forgot your password?

Show

Keep me signed in

No SPIE account? Create an account

Proceedings Article | 15 March 2019 Paper

Deep learning of 3D computed tomography (CT) images for organ segmentation using 2D multi-channel SegNet model

Yingzhou Liu, Wanyi Fu, Vignesh Selvakumaran, Matthew Phelan, W. Paul Segars, Ehsan Samei, Maciej Mazurowski, Joseph Lo, Geoffrey Rubin, Ricardo Henao

Proceedings Volume 10954, 109541D (2019) https://doi.org/10.1117/12.2512887

KEYWORDS: Image segmentation, Data modeling, 3D modeling, Computed tomography, Kidney, 3D image processing, Medical imaging, Machine learning

Read Abstract +

Purpose To accurately segment organs from 3D CT image volumes using a 2D, multi-channel SegNet model consisting of a deep Convolutional Neural Network (CNN) encoder-decoder architecture. Method We trained a SegNet model on the extended cardiac-torso (XCAT) dataset, which was previously constructed based on patient Chest–Abdomen–Pelvis (CAP) Computed Tomography (CT) studies from 50 Duke patients. Each study consists of one low-resolution (5-mm section thickness) 3D CT image volume and its corresponding 3D, manually labeled volume. To improve modeling on such small sample size regime, we performed median frequency class balancing weighting in the loss function of the SegNet, data normalization adjusting for intensity coverage of CT volumes, data transformation to harmonize voxel resolution, CT section extrapolation to virtually increase the number of transverse sections available as inputs to the 2D multi-channel model, and data augmentation to simulate mildly rotated volumes. To assess model performance, we calculated Dice coefficients on a held-out test set, as well as qualitative evaluation of segmentation on high-resolution CTs. Further, we incorporated 50 patients high-resolution CTs with manually-labeled kidney segmentation masks for the purpose of quantitatively evaluating the performance of our XCAT trained segmentation model. The entire study was conducted from raw, identifiable data within the Duke Protected Analytics Computing Environment (PACE). Result We achieved median Dice coefficients over 0.8 for most organs and structures on XCAT test instances and observed good performance on additional images without manual segmentation labels, qualitatively evaluated by Duke Radiology experts. Moreover, we achieved 0.89 median Dice Coefficients for kidneys on high-resolution CTs. Conclusion 2D, multi-channel models like SegNet are effective for organ segmentations of 3D CT image volumes, achieving high segmentation accuracies.

Proceedings Article | 13 March 2019 Presentation + Paper

Combining deep learning methods and human knowledge to identify abnormalities in computed tomography (CT) reports

Matias Benitez, James Tian, Mark Kelly, Vignesh Selvakumaran, Matthew Phelan, Maciej Mazurowski, Joseph Lo, Geoffrey Rubin, Ricardo Henao

Proceedings Volume 10950, 109500V (2019) https://doi.org/10.1117/12.2512886

KEYWORDS: Computed tomography, Kidney, Lung, Performance modeling, Machine learning, Statistical modeling, Medicine

Read Abstract +

Many researchers in the field of machine learning have addressed the problem of detecting anomalies within Computed Tomography (CT) scans. Training these machine learning algorithms requires a dataset of CT scans with identified anomalies (labels), usually, in specific organs. This represents a problem, since it requires experts to review thousands of images in order to create labels for these data. We aim to decrease human burden at labeling CT scans by developing a model that identifies anomalies within plain-text-based reports that then could be further used as a method to create labels for models based on CT scans. This study contains more than 4800 CT reports from Duke Health System, for which we aim to identify organ specific abnormalities. We propose an iterative active learning approach that consists of building a machine learning model to classify CT reports by abnormalities in different organs and then improving it by actively adding reports sequentially. At each iteration, clinical experts review the report that provides the model with highest expected information gain. This process is done in real time by using a web interface. Then, this datum is used by the model to improve its performance. We evaluated the performance of our method for abnormalities in kidneys and lungs. When starting with a model trained on 99 reports, the results show the model achieves an Area Under the Curve (AUC) score of 0.93 on the test set after adding 130 actively labeled reports to the model from an unlabeled pool of 4,000. This suggests that a set of labeled CT scans can be obtained with significantly reduced human work by combining machine learning techniques and clinical experts' knowledge.

My Library

You currently do not have any folders to save your paper to! Create a new folder below.

Folder Name

Folder Description

View contact details

UPDATE YOUR PROFILE

Is this your profile? Update it now.

Sign into your SPIE.org account

Don’t have a profile and want one?

Create an account on SPIE.org

Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks. You are receiving this notice because your organization may not have SPIE eBooks access.*

*Shibboleth/Open Athens users─please sign in to access your institution's subscriptions.

To obtain this item, you may purchase the complete book in print or electronic format on SPIE.org.

ORGANIZATIONAL
Sign in with credentials provided by your organization.

Organizational Username

Organizational Password

Show/Hide Password

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available

Members:

Non-members: ADD TO CART

Keywords/Phrases

Search In:

Publication Years