Automatic Segmentation of Finger Bone Regions from CR Images Using Improved DeepLabv3

The number of hospitalized patients and the number of people requiring nursing care are serious social problems in Japan due to the increasing elderly population. The major causes of bedridden patients are bone and joint disorders caused by rheumatoid arthritis and osteoporosis. Early detection and...

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Vydané v:International Conference on Control, Automation and Systems (Online) s. 1788 - 1791
Hlavní autori: Ono, Hikaru, Murakami, Seiichi, Kamiya, Tohru, Aoki, Takatoshi
Médium: Konferenčný príspevok..
Jazyk:English
Vydavateľské údaje: ICROS 12.10.2021
Predmet:
Bones
Computer-Aided Diagnosis System
DeepLabv3+ Convolutional Neural Network
Image segmentation
Imaging
Medical services
Osteoporosis
Rheumatoid Arthritis
Segmentation
Sociology
Visualization
ISSN:2642-3901
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Popis
Shrnutí:The number of hospitalized patients and the number of people requiring nursing care are serious social problems in Japan due to the increasing elderly population. The major causes of bedridden patients are bone and joint disorders caused by rheumatoid arthritis and osteoporosis. Early detection and treatment of these bone diseases are important because they significantly interfere with the quality of life (QOL) as the symptoms progress. Visual screening based on CR is used as a diagnosing tool for bone diseases. However, imaging diagnosis is subjective and lacks objectivity, and there is a possibility that lesions may be overlooked. In addition, it is difficult to find out subtle changes from images, increasing the workload for doctors. To solve these problems, there is a need to develop a computer aided diagnosis (CAD) system that can quantitatively diagnose bone diseases. We propose a method for automatic extraction of phalange regions for the CAD system to diagnose these diseases. The proposed method can extract the phalanges with high accuracy by using the improved DeepLabv3+. In this paper, we apply the proposed method to 101 cases of CR images and mIoU of 0.949 was obtained.
ISSN:2642-3901
DOI:10.23919/ICCAS52745.2021.9649864