Noninvasive detection and quantification of liver fibrosis from strain-encoded elastography using artificial neural network / (Record no. 170635)
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| 000 -LEADER | |
|---|---|
| fixed length control field | 06552namaa22004091i 4500 |
| 003 - CONTROL NUMBER IDENTIFIER | |
| control field | OSt |
| 005 - أخر تعامل مع التسجيلة | |
| control field | 20250223033426.0 |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION | |
| fixed length control field | 250205s2024 |||a|||fr|||| 000 0 eng d |
| 040 ## - CATALOGING SOURCE | |
| Original cataloguing agency | EG-GICUC |
| Language of cataloging | eng |
| Transcribing agency | EG-GICUC |
| Modifying agency | EG-GICUC |
| Description conventions | rda |
| 041 0# - LANGUAGE CODE | |
| Language code of text/sound track or separate title | eng |
| Language code of summary or abstract | eng |
| -- | ara |
| 049 ## - Acquisition Source | |
| Acquisition Source | Deposit |
| 082 04 - DEWEY DECIMAL CLASSIFICATION NUMBER | |
| Classification number | 610.28 |
| 092 ## - LOCALLY ASSIGNED DEWEY CALL NUMBER (OCLC) | |
| Classification number | 610.28 |
| Edition number | 21 |
| 097 ## - Degree | |
| Degree | M.Sc |
| 099 ## - LOCAL FREE-TEXT CALL NUMBER (OCLC) | |
| Local Call Number | Cai01.13.03.M.Sc.2024.Pe.N |
| 100 0# - MAIN ENTRY--PERSONAL NAME | |
| Authority record control number or standard number | Peter Emad Salah, |
| Preparation | preparation. |
| 245 10 - TITLE STATEMENT | |
| Title | Noninvasive detection and quantification of liver fibrosis from strain-encoded elastography using artificial neural network / |
| Statement of responsibility, etc. | by Peter Emad Salah ; Under the Supervision of Prof. Dr. Inas Ahmed Yassine. |
| 246 15 - VARYING FORM OF TITLE | |
| Title proper/short title | الاكتشاف والتحديد الكمي بطرق غير جراحية للتليف الكبدي من التصوير الإلستوجرافي المدمج به معلومات صلابة الكبد باستخدام شبكة عصبية اصطناعية / |
| 264 #0 - PRODUCTION, PUBLICATION, DISTRIBUTION, MANUFACTURE, AND COPYRIGHT NOTICE | |
| Date of production, publication, distribution, manufacture, or copyright notice | 2024. |
| 300 ## - PHYSICAL DESCRIPTION | |
| Extent | 63 pages : |
| Other physical details | illustrations ; |
| Dimensions | 30 cm. + |
| Accompanying material | CD. |
| 336 ## - CONTENT TYPE | |
| Content type term | text |
| Source | rda content |
| 337 ## - MEDIA TYPE | |
| Media type term | Unmediated |
| Source | rdamedia |
| 338 ## - CARRIER TYPE | |
| Carrier type term | volume |
| Source | rdacarrier |
| 502 ## - DISSERTATION NOTE | |
| Dissertation note | Thesis (M.Sc.)-Cairo University, 2023. |
| 504 ## - BIBLIOGRAPHY, ETC. NOTE | |
| Bibliography, etc. note | Bibliography: pages 49-55. |
| 520 ## - SUMMARY, ETC. | |
| Summary, etc. | Liver fibrosis is a common reaction to very different diseases and infection of liver<br/>tissue. Several factors have caused liver fibrosis to spread all over the world. In some<br/>countries, food and water contaminants have caused large amounts of liver infections [1].<br/>In other areas of the world wrong diet habits that cause obesity or increase the amount of<br/>alcohol consumption can lead to liver fibrosis too [2]. Liver diseases are very common<br/>especially in third world countries where hepatitis B and hepatitis C only are widely<br/>spread affecting more that 429 million patients while the percentage of diagnosed people<br/>does not exceed 10% [3].<br/>In particular, Chronic Liver Diseases (CLD) are estimated to affect 1.5 billion people<br/>worldwide. Progression of CLD leads to excessive fibrosis and cirrhosis which could<br/>lead to hepatocellular carcinoma. Accurate diagnosis and staging of fibrosis are essential<br/>for proper disease prognosis and progression monitoring [4]. Liver biopsy is considered<br/>the gold standard for grading and staging of fibrosis [5]. However, it is an invasive and<br/>expensive procedure that could cause complications, pain for the patient, and suffers from<br/>sampling errors and observer variability.<br/>Alternatively, Magnetic Resonance Elastography (MRE) is considered the best<br/>noninvasive technique for LSM and fibrosis grading [6]. MRE produces strain-encoded<br/>images indicating the Liver Stiffness Measurement (LSM) distribution in the liver and<br/>they significantly correlate with biopsy results without suffering from observer<br/>variability. However, MRE staging is limited by different cut-off thresholds in different<br/>studies and requires radiologists to manually select a valid ROI from the liver for LSM<br/>estimation. Consequently, a pipeline is presented to automatically segment the liver from<br/>an MRE scan, select a high-quality Region of Interest (ROI) and estimate LSM without<br/>any radiologist intervention. First, a U-Net was used to segment the liver from the MRE<br/>magnitude images. Then, the MRE Non-linearity images were used as confidence map<br/>indicators to determine the high SNR regions. Then they were thresholded using Otsu<br/>algorithm and then inverted to mark high quality regions. Additionally, in order to extract<br/>a high-quality ROI, the Non-linearity output and the liver masks were combined together.<br/>Finally, the high-quality ROIs from Gelastic, Velocity, Viscosity and Elasticity MRE<br/>strain-encoded images were used to calculate the LSM using weighted averaging. The<br/>extracted measurements were used to feed an Artificial Neural Networks (ANN) for the<br/>automatic grading of liver fibrosis, with liver biopsy grades as the true labels.<br/>The LSM estimation reached correlations of 0.92, 0.89, 0.90, 0.81 with the<br/>radiologists’ estimation using the MRE Gelastic, viscosity, elasticity, and velocity<br/>images respectively. The ANN was able to achieve accuracy of 85% for fibrosis grading<br/>using conventional train, validation, test data splitting. Finally, a data sampling approach<br/>is used to increase the amount of data with high-quality samples. The proposed technique<br/>allows further experiments and investigations. It has been then used along with neural<br/>network search to compare between different approaches that aim to find the optimum<br/>system that is capable of providing an accurate and robust system for liver fibrosis<br/>grading. It was found that patch-based training with CNN can provide the most robust<br/>system and the combination of Elasticity, Velocity and Viscosity can provide the best<br/>combination toward highest accuracy |
| 520 ## - SUMMARY, ETC. | |
| Summary, etc. | الأمراض المزمنة للكبد تؤثر على 1.5 مليار شخص حول العالم وتؤدي إلى تليّف وسرطان الكبد، لذلك التشخيص الصحيح مطلوب. ولذلك، يقدم هذا البحث حلا لتجزئة الكبد تلقائيًا، واختيار منطقة ذات جودة عالية من الاهتمام، وتقدير معامل مرونة الكبد بدون أي تدخل من أخصائي الأشعة. يتم استخراج معلومات مرونة الكبد من صور الاشعة ومن ثم إرسالها إلى شبكة عصبية اصطناعية لتقدير تصنيف التليف الكبدي تلقائيًا. وحققت الشبكة العصبية الاصطناعية دقة بلغت 85% في تصنيف التليف الكبدي. وأخيرًا، يتم استخدام تقنية لزيادة كمية العينات ذات الجودة العالية فتمكنا من إجراء تجارب و استقصاءات إضافية للعثور على النظام الأمثل والأوفر دقيقًة لتصنيف التليف الكبدي. |
| 530 ## - ADDITIONAL PHYSICAL FORM AVAILABLE NOTE | |
| Issues CD | Issued also as CD |
| 546 ## - LANGUAGE NOTE | |
| Text Language | Text in English and abstract in Arabic & English. |
| 650 #7 - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical term or geographic name entry element | Biomedical Engineering |
| Source of heading or term | qrmak |
| 653 #0 - INDEX TERM--UNCONTROLLED | |
| Uncontrolled term | liver fibrosis |
| -- | magnetic resonance elastography |
| -- | liver stiffness measurement |
| -- | elastogram |
| -- | artificial neural networks |
| 700 0# - ADDED ENTRY--PERSONAL NAME | |
| Personal name | Inas Ahmed Yassine |
| Relator term | thesis advisor. |
| 900 ## - Thesis Information | |
| Grant date | 01-01-2024 |
| Supervisory body | Inas Ahmed Yassine |
| Discussion body | Ahmed Hisham Kandil |
| -- | Nancy Mustafa Salem |
| Universities | Cairo University |
| Faculties | Faculty of Engineering |
| Department | Department of Biomedical Engineering and Systems |
| 905 ## - Cataloger and Reviser Names | |
| Cataloger Name | Eman Ghareeb |
| Reviser Names | Huda |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) | |
| Source of classification or shelving scheme | Dewey Decimal Classification |
| Koha item type | Thesis |
| Edition | 21 |
| Suppress in OPAC | No |
| Source of classification or shelving scheme | Home library | Current library | Date acquired | Inventory number | Full call number | Barcode | Date last seen | Effective from | Koha item type |
|---|---|---|---|---|---|---|---|---|---|
| Dewey Decimal Classification | المكتبة المركزبة الجديدة - جامعة القاهرة | قاعة الرسائل الجامعية - الدور الاول | 05.02.2025 | 90525 | Cai01.13.03.M.Sc.2024.Pe.N | 01010110090525000 | 05.02.2025 | 05.02.2025 | Thesis |