Improving CT Image Tumor Segmentation Through Deep Supervision and Attentional Gates

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F70883521%3A28140%2F20%3A63525359" target="_blank" >RIV/70883521:28140/20:63525359 - isvavai.cz</a>

Result on the web

<a href="https://www.frontiersin.org/articles/10.3389/frobt.2020.00106/full" target="_blank" >https://www.frontiersin.org/articles/10.3389/frobt.2020.00106/full</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.3389/frobt.2020.00106" target="_blank" >10.3389/frobt.2020.00106</a>

Result language

angličtina

Original language name

Improving CT Image Tumor Segmentation Through Deep Supervision and Attentional Gates

Original language description

Computer Tomography (CT) is an imaging procedure that combines many X-ray measurements taken from different angles. The segmentation of areas in the CT images provides a valuable aid to physicians and radiologists in order to better provide a patient diagnose. The CT scans of a body torso usually include different neighboring internal body organs. Deep learning has become the state-of-the-art in medical image segmentation. For such techniques, in order to perform a successful segmentation, it is of great importance that the network learns to focus on the organ of interest and surrounding structures and also that the network can detect target regions of different sizes. In this paper, we propose the extension of a popular deep learning methodology, Convolutional Neural Networks (CNN), by including deep supervision and attention gates. Our experimental evaluation shows that the inclusion of attention and deep supervision results in consistent improvement of the tumor prediction accuracy across the different datasets and training sizes while adding minimal computational overhead.

Czech name

—

Czech description

—

Type

J_SC - Article in a specialist periodical, which is included in the SCOPUS database

CEP classification

—

OECD FORD branch

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Project

—

Continuities

S - Specificky vyzkum na vysokych skolach

Publication year

2020

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Name of the periodical

Frontiers Robotics AI

ISSN

2296-9144

e-ISSN

—

Volume of the periodical

7

Issue of the periodical within the volume

Neuveden

Country of publishing house

CH - SWITZERLAND

Number of pages

14

Pages from-to

1-14

UT code for WoS article

000570414900001

EID of the result in the Scopus database

2-s2.0-85090765676