Mr. Ryan P. Sullivan Profile

Ryan P. Sullivan

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 | 16 March 2020 Paper

Deep learning methods for segmentation of lines in pediatric chest radiographs

Ryan Sullivan, Greg Holste, Jonathan Burkow, Adam Alessio

Proceedings Volume 11314, 113142I (2020) https://doi.org/10.1117/12.2550686

KEYWORDS: Image segmentation, Computer programming, Chest imaging, Statistical modeling, Data modeling, Performance modeling, Image classification

Read Abstract +

Surgical procedures often require the use of catheters, tubes, and lines, collectively called lines. Misplaced lines can cause serious complications, such as pneumothorax, cardiac perforation, or thrombosis. To prevent these problems, radiologists examine chest radiographs after insertion and throughout intensive care to evaluate their placement. This process is time consuming, and incorrect interpretations occur with notable frequency. Fast and reliable automatic interpretations could potentially reduce the cost of these surgical operations, decrease the workload of radiologists, and improve the quality of care for patients. We develop a segmentation model which can highlight the medically relevant lines in pediatric chest radiographs using deep learning. We propose a two-stage segmentation network which first classifies whether images have medically relevant lines and then segments images with lines. For the segmentation stage, we use the popular U-Net architecture substituting the encoder path with multiple state-of-the-art CNN encoders. Our study compares the performance of different permutations of model architectures for the task of highlighting lines in pediatric chest radiographs and demonstrates the effectiveness of the two-stage architecture.

Proceedings Article | 10 March 2020 Presentation + Paper

Multi-class semantic segmentation of pediatric chest radiographs

Gregory Holste, Ryan Sullivan, Michael Bindschadler, Nicholas Nagy, Adam Alessio

Proceedings Volume 11313, 113131E (2020) https://doi.org/10.1117/12.2544426

KEYWORDS: Image segmentation, Radiography, Chest imaging, Performance modeling, Data modeling, Chest, Spine, Lung, Binary data

Read Abstract +

Chest radiographs are a common diagnostic tool in pediatric care, and several computer-augmented decision tasks for radiographs would benefit from knowledge of the anatomic locations within the thorax. For example, a pre-segmented chest radiograph could provide context for algorithms designed for automatic grading of catheters and tubes. This work develops a deep learning approach to automatically segment chest radiographs into multiple regions to provide anatomic context for future automatic methods. This type of segmentation offers challenging aspects in its goal of multi-class segmentation with extreme class imbalance between regions. In an IRB-approved study, pediatric chest radiographs were collected and annotated with custom software in which users drew boundaries around seven regions of the chest: left and right lung, left and right subdiaphragm, spine, mediastinum, and carina. We trained a U-Net-style architecture on 328 annotated radiographs, comparing model performance with various combinations of loss functions, weighting schemes, and data augmentation. On a test set of 70 radiographs, our best-performing model achieved 93.8% mean pixel accuracy and a mean Dice coefficient of 0.83. We find that (1) cross-entropy consistently outperforms generalized Dice loss, (2) light augmentation, including random rotations, improves overall performance, and (3) pre-computed pixel weights that account for class frequency provide small performance boosts. Overall, our approach produces realistic eight-class chest segmentations that can provide anatomic context for line placement and potentially other medical applications.

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