A Review on Deep Convolutional Neural Network Architectures for Medical Image Segmentation

• Published in: Lecture Notes in Electrical Engineering
• Main Author: Awang Mustapa N.H.; Mat Som M.H.; Basaruddin K.S.; Megat Ali M.S.A.
• Format: Conference paper
• Language: English
• Published: Springer Science and Business Media Deutschland GmbH 2022
• Online Access: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85125284492&doi=10.1007%2f978-981-16-8129-5_148&partnerID=40&md5=2fbb20fafc7e5508551ba4e61ab1d9af
• ISSN: 18761100
• DOI: 10.1007/978-981-16-8129-5_148

Osteogenesis Imperfecta (OI) image segmentation by using Deep Convolutional Neural Network (DCNN) is yet to be evaluated. The segmentation of OI is very important as a useful tool for medical experts to further analyze the fracture risk and avoid bone fractures. In this paper, we present the review of DCNN architecture used in image segmentation. The images were obtained from different types of modalities such as Computed Tomography (CT), Magnetic Resonance Imaging (MRI), or Ultrasound. Several architectures have been used by previous studies include U-Net, faster R-CNN, ResNet, and MS-Net architecture to automatically segment the images. Overall, all researchers from the reviewed papers concluded that the proposed DCNN architecture gave good performance results. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.