TY - GEN
T1 - Binary Seeds Auto Generation Model for Knee Cartilage Segmentation
AU - Gan, Hong Seng
AU - Mohd Rosidi, Rasyiqah Annani
AU - Hamidur, Haziqah
AU - Sayuti, Khairil Amir
AU - Ramlee, Muhammad Hanif
AU - Abdul Karim, Ahmad Helmy
AU - Abd Salam, Bakthiar Al Jefry
N1 - Publisher Copyright:
© 2018 IEEE.
PY - 2018/11/19
Y1 - 2018/11/19
N2 - Segmentation is an instrumental task in medical image analysis. In addition to existing manual, semi-automatic and automatic segmentation models, deep learning has been the niftiest machine learning technique in current research interests. However, none of the models or technique can escape from the overdependence on training data and user intervention. As a result, the use of computer-aided and learning algorithms have reported lackluster robustness in the presence of high anatomical disparity. In recognition of this, we have proposed a binary seeds auto-generation model to reduce the reliance on manually crafted priori information in deep learning. Then, we computed the reproducibility of the proposed model against manual segmentation using normal and osteoarthritic knee magnetic resonance image. In normal knee image, mean agreements of the proposed model and manual segmentation were 0.94 pm 0.022 and 0.83pm 0.028 respectively. In osteoarthritic knee image, mean agreements of the proposed model and manual segmentation were 0.92pm 0.051 and mathbf 0.79pm0.073 respectively. Pair t test showed that our method has better accuracy than manual segmentation in both cases (normal: P=1.03×10 -9 osteoarthritic: P=4.94×10 -8 ). Therefore, we can conclude the model is robust to be implemented as part of deep learning based segmentation framework.
AB - Segmentation is an instrumental task in medical image analysis. In addition to existing manual, semi-automatic and automatic segmentation models, deep learning has been the niftiest machine learning technique in current research interests. However, none of the models or technique can escape from the overdependence on training data and user intervention. As a result, the use of computer-aided and learning algorithms have reported lackluster robustness in the presence of high anatomical disparity. In recognition of this, we have proposed a binary seeds auto-generation model to reduce the reliance on manually crafted priori information in deep learning. Then, we computed the reproducibility of the proposed model against manual segmentation using normal and osteoarthritic knee magnetic resonance image. In normal knee image, mean agreements of the proposed model and manual segmentation were 0.94 pm 0.022 and 0.83pm 0.028 respectively. In osteoarthritic knee image, mean agreements of the proposed model and manual segmentation were 0.92pm 0.051 and mathbf 0.79pm0.073 respectively. Pair t test showed that our method has better accuracy than manual segmentation in both cases (normal: P=1.03×10 -9 osteoarthritic: P=4.94×10 -8 ). Therefore, we can conclude the model is robust to be implemented as part of deep learning based segmentation framework.
UR - http://www.scopus.com/inward/record.url?scp=85059733846&partnerID=8YFLogxK
U2 - 10.1109/ICIAS.2018.8540570
DO - 10.1109/ICIAS.2018.8540570
M3 - Conference Proceeding
AN - SCOPUS:85059733846
T3 - International Conference on Intelligent and Advanced System, ICIAS 2018
BT - International Conference on Intelligent and Advanced System, ICIAS 2018
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 7th International Conference on Intelligent and Advanced System, ICIAS 2018
Y2 - 13 August 2018 through 14 August 2018
ER -