應用U-NET和Mask R-CNN於鼓膜影像切割及破洞特徵提取實踐居家聽力功能預測及中耳炎自動輔助診斷系統

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姓名

郭又榕(Yu-Jung Kuo) 查詢紙本館藏

畢業系所

生物醫學工程研究所

論文名稱

應用U-NET和Mask R-CNN於鼓膜影像切割及破洞特徵提取實踐居家聽力功能預測及中耳炎自動輔助診斷系統
(Home-Based Hearing Loss Prediction and Otitis Media Diagnosis Using U-Net and Mask R-CNN for Tympanic Membrane Image Analysis and Perforation Detection)

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至系統瀏覽論文 (2029-7-31以後開放)

摘要(中)

世界衛生組織於2024年2月發表的耳聾和聽力損失報告書中指出，估計於2050年將有超過7億人次發生殘疾性聽力損失。其中，中耳炎是造成殘疾性聽損的原因之一。中耳炎是常見的感染性疾病，根據病程和症狀，可以分為急性、積液性和慢性三類。急性中耳炎通常好發於兒童，但偶而也會發生於成人，特別在季節交替時期，尤為嚴重。慢性化膿性中耳炎則是感染後，持續數週甚至更久的發炎，可能導致鼓膜穿孔。由於患病嚴重者將可能造成永久聽力損害或其他併發症狀，對病患來日生活影響甚深。因此，迫切需要能即時輔助檢測中耳炎病灶之方法。隨著遠程醫療的快速發展，全球許多團隊紛紛開發出各式新型以深度學習為基礎的鼓膜影像輔助診斷策略。
本研究的目的亦利用深度學習和影像處理的技術，建立鼓膜分割與電腦輔助診斷系統。此系統技術的核心為建立中耳炎資料庫及即時影像分割系統，實現輔助診斷系統分割鼓膜構造。影像分割所使用的資料來自經國泰醫療財團法人國泰綜合醫院人體試驗審查委員會申請核可計畫編號批准 (IRB編號CGH-CS109004)，共1014張鼓膜影像，研究中比較了U-Net及Mask R-CNN於鼓膜及鼓膜穿孔上的分割結果，Mask R-CN於鼓膜及穿孔上之MIoU分別為0.88及0.87。
此外，本研究還初步探討了慢性中耳炎影像與傳導性聽力損失之間的關聯性研究，以拓展系統的應用範圍。使用經國泰醫療財團法人國泰綜合醫院人體試驗審查委員會申請核可計畫批准 (IRB編號CGH-P113003)，研究於2024年4月迄今，研究旨於估計慢性中耳炎之鼓膜破損造成的傳導性聽力受損，比較了基於聲壓差之估計方法與線性回歸方法於聽力損失之表現。

摘要(英)

The World Health Organization′s report on deafness and hearing loss published in February 2024 stated that it is estimated that more than 700 million people will suffer from disabling hearing loss in 2050. One of the causes of disabling hearing loss is otitis media. Otitis media is a common infectious disease that can be classified into three types based on its course and symptoms: acute, otitis media with effusion, and chronic. Acute otitis media commonly occurs in children but can occasionally affect adults, especially during seasonal transitions, where it can be particularly severe. Chronic suppurative otitis media involves persistent inflammation lasting for weeks or longer after an infection, potentially leading to tympanic membrane perforation. Severe cases can result in permanent hearing damage or other complications, significantly affecting the patient′s future life. Therefore, there is an urgent need for methods that can assist in the timely detection of otitis media lesions. With the rapid development of telemedicine, many teams worldwide are developing various new tympanic membrane imaging diagnostic strategies based on deep learning.
The purpose of this study is to use deep learning and image processing technologies to establish a tympanic membrane segmentation and computer-aided diagnosis system. The core of this system′s technology is the creation of an otitis media database and a real-time image segmentation system to enable the computer-aided diagnosis system to segment tympanic membrane structures. The image segmentation data comes from a project approved by the Institutional Review Board of Cathay General Hospital (IRB number CGH-CS109004), consisting of 1,014 tympanic membrane images. We compared the segmentation results of U-Net and Mask R-CNN on tympanic membranes and perforations, with Mask R-CNN achieving MIoU scores of 0.88 and 0.87 on the tympanic membrane and perforations, respectively.
Additionally, this study preliminarily explored the correlation between chronic otitis media images and conductive hearing loss to expand the system′s application range. Using data from another project approved by the Institutional Review Board of Cathay General Hospital (IRB number CGH-P113003), conducted from April 2024 to the present, the study aims to estimate the conductive hearing impairment caused by tympanic membrane damage in chronic otitis media. It compares the performance of air pressure difference-based estimation methods and linear regression methods in evaluating hearing loss.

關鍵字(中)

★ 慢性中耳炎
★ 耳鏡
★ 影像分割

關鍵字(英)

★ Chronic otitis media(COM)
★ Otoscope
★ Segmentation

論文目次

摘要 i
ABSTRACT ii
目錄 iv
表目錄 vi
圖目錄 vii
第一章緒論 1
1-1 序言 1
1-2 研究目的 1
1-3 論文架構 2
第二章文獻探討 3
2-1 中耳與聽力學 3
2-1-1 中耳炎疾病分類 4
2-1-2 聽力損失 4
2-2 純音聽力測試 5
2-3 電腦視覺及影像處理 7
2-3-1 影像分割及分割模型 8
2-4 相關研究 12
2-4-1 深度學習應用於鼓膜影像分割中耳炎診斷 12
2-4-2 文獻回顧結語 13
第三章基於深度學習與數位耳鏡開發中耳炎輔助診斷系統 15
3-1 影像分割模型訓練 15
3-1-1 資料來源及相關統計分析 15
3-1-2 Preprocessing 15
3-1-3 實作 18
3-2 研究結果與討論 19
3-2-1 檢測指標 19
3-3 結語 21
第四章鼓膜影像與傳導性聽力損失相關研究 22
4-1 定義 22
4-2 資料來源及收案標準 24
4-2-1 案例類型 24
4-2-2 Data Preprocessing and data cleaning 25
4-3 案例統計分析 26
4-3-1 基於聲壓差猜測傳導性聽力損失 27
4-3-2 Correlation Analysis 28
4-3-3 Linear Regression 30
4-3-4 評估指標 31
4-3-5 Pinhole Perforation and Inflammation Cases 33
4-3-6 估計值與實際值之關係 35
第五章結論 37
Reference 38

參考文獻

1. Kashani, R.G., et al., Shortwave infrared otoscopy for diagnosis of middle ear effusions: A machine-learning-based approach. Scientific Reports, 2021. 11(1): p. 12509.
2. Habib, A.-R., et al., Evaluating the generalizability of deep learning image classification algorithms to detect middle ear disease using otoscopy. Scientific reports, 2023. 13(1): p. 5368.
3. Fang, T.Y., et al., Algorithm-Driven Tele-otoscope for Remote Care for Patients With Otitis Media. Otolaryngol Head Neck Surg, 2024. 170(6): p. 1590-1597.
4. Suresh, K., et al., Generation of synthetic tympanic membrane images: Development, human validation, and clinical implications of synthetic data. PLOS Digit Health, 2023. 2(2): p. e0000202.
5. Shaikh, N., et al., Development and Validation of an Automated Classifier to Diagnose Acute Otitis Media in Children. JAMA Pediatr, 2024. 178(4): p. 401-407.
6. Zeng, J., et al., A Deep Learning Approach to Predict Conductive Hearing Loss in Patients With Otitis Media With Effusion Using Otoscopic Images. JAMA Otolaryngol Head Neck Surg, 2022. 148(7): p. 612-620.
7. Rosenfeld, R.M., et al., Clinical Practice Guideline: Otitis Media with Effusion (Update). Otolaryngol Head Neck Surg, 2016. 154(1 Suppl): p. S1-s41.
8. 劉得懿 and 王拔群, 內視鏡內嵌式軟骨鼓室成形術：單一醫院經驗. 台灣耳鼻喉頭頸外科雜誌, 2021. 56(4): p. 223-229.
9. Wang, P.C., et al., Incidence and recurrence of acute otitis media in Taiwan′s pediatric population. Clinics (Sao Paulo), 2011. 66(3): p. 395-9.
10. Teele, D.W., J.O. Klein, and B. Rosner, Epidemiology of otitis media during the first seven years of life in children in greater Boston: a prospective, cohort study. J Infect Dis, 1989. 160(1): p. 83-94.
11. Minor, L.B., et al., Sound- and/or pressure-induced vertigo due to bone dehiscence of the superior semicircular canal. Arch Otolaryngol Head Neck Surg, 1998. 124(3): p. 249-58.
12. Minor, L.B., et al., Dehiscence of bone overlying the superior canal as a cause of apparent conductive hearing loss. Otol Neurotol, 2003. 24(2): p. 270-8.
13. 方麗娟, 洪朝明, and 黃世鴻, 耳硬化症─25年鐙骨手術經驗. 台灣耳鼻喉頭頸外科雜誌, 2011. 46(6): p. 304-309.
14. Ahmad, S.W. and G.V. Ramani, Hearing loss in perforations of the tympanic membrane. J Laryngol Otol, 1979. 93(11): p. 1091-8.
15. Mehta, R.P., et al., Determinants of Hearing Loss in Perforations of the Tympanic Membrane. Otology & Neurotology, 2006. 27(2): p. 136-143.
16. Hsu, C.Y., et al., A computer program to calculate the size of tympanic membrane perforations. Clin Otolaryngol Allied Sci, 2004. 29(4): p. 340-2.
17. Black, J., S. Hickey, and P. Wormald, An analysis of the results of myringoplasty in children. International journal of pediatric otorhinolaryngology, 1995. 31(1): p. 95-100.
18. Kolluru, K., S. Kumar, and P. Upadhyay, A Study of Correlation Between Tympanic Membrane Perforation Size With Hearing Loss in Patients With Inactive Mucosal Chronic Otitis Media. Otol Neurotol, 2021. 42(1): p. e40-e44.
19. Ronneberger, O., P. Fischer, and T. Brox. U-net: Convolutional networks for biomedical image segmentation. in Medical image computing and computer-assisted intervention–MICCAI 2015: 18th international conference, Munich, Germany, October 5-9, 2015, proceedings, part III 18. 2015. Springer.
20. He, K., et al. Mask r-cnn. in Proceedings of the IEEE international conference on computer vision. 2017.
21. Girshick, R., et al. Rich feature hierarchies for accurate object detection and semantic segmentation. in Proceedings of the IEEE conference on computer vision and pattern recognition. 2014.
22. Girshick, R. Fast r-cnn. in Proceedings of the IEEE international conference on computer vision. 2015.
23. Ren, S., et al., Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems, 2015. 28.
24. Pham, V.T., et al., EAR-UNet: A deep learning-based approach for segmentation of tympanic membranes from otoscopic images. Artif Intell Med, 2021. 115: p. 102065.
25. Pham, V.-T., et al., Tympanic membrane segmentation in otoscopic images based on fully convolutional network with active contour loss. Signal, Image and Video Processing, 2021. 15(3): p. 519-527.
26. Kim, T., et al., Development of ResNet152 UNet++-Based Segmentation Algorithm for the Tympanic Membrane and Affected Areas. IEEE Access, 2023. 11: p. 56225-56234.
27. Seok, J., et al., The semantic segmentation approach for normal and pathologic tympanic membrane using deep learning. BioRxiv, 2019: p. 515007.
28. Park, Y.S., et al., Deep Learning Techniques for Ear Diseases Based on Segmentation of the Normal Tympanic Membrane. Clin Exp Otorhinolaryngol, 2023. 16(1): p. 28-36.
29. Khan, M.A., et al., Automatic detection of tympanic membrane and middle ear infection from oto-endoscopic images via convolutional neural networks. Neural Netw, 2020. 126: p. 384-394.
30. Tran, T.-T., et al., Development of an Automatic Diagnostic Algorithm for Pediatric Otitis Media. Otology & Neurotology, 2018. 39(8): p. 1060-1065.
31. Byun, H., et al., Automatic Prediction of Conductive Hearing Loss Using Video Pneumatic Otoscopy and Deep Learning Algorithm. Ear Hear, 2022. 43(5): p. 1563-1573.
32. Kim, T., et al., Toward Better Ear Disease Diagnosis: A Multi-Modal Multi-Fusion Model Using Endoscopic Images of the Tympanic Membrane and Pure-Tone Audiometry. IEEE Access, 2023. 11: p. 116721-116731.
33. CARHART, R., CLINICAL APPLICATION OF BONE CONDUCTION AUDIOMETRY. Archives of Otolaryngology, 1950. 51(6): p. 798-808.
34. SHEEHY, J.L. and W.F. HOUSE, Tympanosclerosis. Archives of Otolaryngology, 1962. 76(2): p. 151-157.
35. Voss, S.E., et al., Middle-ear function with tympanic-membrane perforations. II. A simple model. J Acoust Soc Am, 2001. 110(3 Pt 1): p. 1445-52.
36. Voss, S.E., et al., Middle-ear function with tympanic-membrane perforations. I. Measurements and mechanisms. J Acoust Soc Am, 2001. 110(3 Pt 1): p. 1432-44.

指導教授

林澂(Chen Lin)

審核日期

2024-7-29

推文