TY - GEN
T1 - Classification of primary cilia in microscopy images using convolutional neural random forests
AU - Ram, Sundaresh
AU - Majdi, Mohammed S.
AU - Rodriguez, Jeffrey J.
AU - Gao, Yang
AU - Brooks, Heddwen L.
N1 - Publisher Copyright:
© 2018 IEEE.
PY - 2018/9/21
Y1 - 2018/9/21
N2 - Accurate detection and classification of primary cilia in microscopy images is an essential and fundamental task for many biological studies including diagnosis of primary ciliary dyskinesia. Manual detection and classification of individual primary cilia by visual inspection is time consuming, and prone to induce subjective bias. However, automation of this process is challenging as well, due to clutter, bleed-through, imaging noise, and the similar characteristics of the non-cilia candidates present within the image. We propose a convolutional neural random forest classifier that combines a convolutional neural network with random decision forests to classify the primary cilia in fluorescence microscopy images. We compare the performance of the proposed classifier with that of an unsupervised k-means classifier and a supervised multi-layer perceptron classifier on real data consisting of 8 representative cilia images, containing more than 2300 primary cilia using precision/recall rates, ROC curves, AUC, and Fβ-score for classification accuracy. Results show that our proposed classifier achieves better classification accuracy.
AB - Accurate detection and classification of primary cilia in microscopy images is an essential and fundamental task for many biological studies including diagnosis of primary ciliary dyskinesia. Manual detection and classification of individual primary cilia by visual inspection is time consuming, and prone to induce subjective bias. However, automation of this process is challenging as well, due to clutter, bleed-through, imaging noise, and the similar characteristics of the non-cilia candidates present within the image. We propose a convolutional neural random forest classifier that combines a convolutional neural network with random decision forests to classify the primary cilia in fluorescence microscopy images. We compare the performance of the proposed classifier with that of an unsupervised k-means classifier and a supervised multi-layer perceptron classifier on real data consisting of 8 representative cilia images, containing more than 2300 primary cilia using precision/recall rates, ROC curves, AUC, and Fβ-score for classification accuracy. Results show that our proposed classifier achieves better classification accuracy.
KW - Image classification
KW - confocal microscopy
KW - convolutional neural network
KW - primary cilia
KW - random forests
UR - http://www.scopus.com/inward/record.url?scp=85055532761&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85055532761&partnerID=8YFLogxK
U2 - 10.1109/SSIAI.2018.8470320
DO - 10.1109/SSIAI.2018.8470320
M3 - Conference contribution
AN - SCOPUS:85055532761
T3 - Proceedings of the IEEE Southwest Symposium on Image Analysis and Interpretation
SP - 89
EP - 92
BT - 2018 IEEE Southwest Symposium on Image Analysis and Interpretation, SSIAI 2018 - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2018 IEEE Southwest Symposium on Image Analysis and Interpretation, SSIAI 2018
Y2 - 8 April 2018 through 10 April 2018
ER -