U-net Based Autonomous Fetal Segmentation From 2D and 3D Ultrasound Images
This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science, 2022.
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Brac University
2022
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10361-176472022-12-13T21:01:48Z U-net Based Autonomous Fetal Segmentation From 2D and 3D Ultrasound Images Mithila, Maliha Tabassum Prome, Tasnim Ahsan Tabassum, Elmi Kamrul, Sameha samiha, Nausheen Rabiul Alam, Dr. Md. Golam Department of Computer Science and Engineering, Brac University Biometric Parameters Gestational Age U-net Semantic Segmentation Head Circumference Abdominal Circumference Femur Length Deep Neural Network Convolution Autonomous Image processing -- Digital techniques Neural networks (Computer science) This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science, 2022. Cataloged from PDF version of thesis. Includes bibliographical references (pages 44-45). There are various biometric parameters of the fetus that need to be evaluated to monitor prenatal diagnosis during pregnancy. Biometric parameters such as head circumference, abdominal circumference, cortical volume, the volume of the brain, crown-rump length, femur length, etc. play a very important part in the character ization and detection of the development of the fetus. Gestational age is one of the most effective parameters for monitoring fetal growth and development, as well as diagnosing any abnormalities, among several quantitative indices. To estimate the gestational age, birth size, weight, and to monitor prenatal abnormalities, many bio metric parameters such as head circumference (HC), abdomen circumference (AC), and femur length (FL) must be measured. We can extract these parameters from the segmentation of an MRI scan. However, performing full manual segmentation is exhaustive and time-consuming. Ultrasound imaging has been shown to be more efficient than MRI for measuring such biometric characteristics.. Also, in this case, manual segmentation requires experts’ experience and skills, clinical experience of the staff which is time-consuming. As a result, we propose a fully autonomous segmentation method based on U-Net architecture for fetal biometric parameters such as head circumference (HC), abdomen circumference (AC), and femur length (FL), which eliminates the need for manual intervention, reduces computational complexity, and greatly speeds up the segmentation process. U-Net is a convolu tional neural network that was created for performing segmentation on biomedical images. Our goal is to train the network such that it can create high-resolution 2D and 3D ultrasound images of each segmented fetal area. Maliha Tabassum Mithila Tasnim Ahsan Prome Elmi Tabassum Sameha Kamrul Nausheen samiha B. Computer Science 2022-12-13T06:24:21Z 2022-12-13T06:24:21Z 2022 2022-05 Thesis ID: 18101459 ID: 18101420 ID: 18101222 ID: 18101523 ID: 18101108 http://hdl.handle.net/10361/17647 en_US Brac University theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. 45 Pages application/pdf Brac University |
| institution |
Brac University |
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Institutional Repository |
| language |
en_US |
| topic |
Biometric Parameters Gestational Age U-net Semantic Segmentation Head Circumference Abdominal Circumference Femur Length Deep Neural Network Convolution Autonomous Image processing -- Digital techniques Neural networks (Computer science) |
| spellingShingle |
Biometric Parameters Gestational Age U-net Semantic Segmentation Head Circumference Abdominal Circumference Femur Length Deep Neural Network Convolution Autonomous Image processing -- Digital techniques Neural networks (Computer science) Mithila, Maliha Tabassum Prome, Tasnim Ahsan Tabassum, Elmi Kamrul, Sameha samiha, Nausheen U-net Based Autonomous Fetal Segmentation From 2D and 3D Ultrasound Images |
| description |
This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science, 2022. |
| author2 |
Rabiul Alam, Dr. Md. Golam |
| author_facet |
Rabiul Alam, Dr. Md. Golam Mithila, Maliha Tabassum Prome, Tasnim Ahsan Tabassum, Elmi Kamrul, Sameha samiha, Nausheen |
| format |
Thesis |
| author |
Mithila, Maliha Tabassum Prome, Tasnim Ahsan Tabassum, Elmi Kamrul, Sameha samiha, Nausheen |
| author_sort |
Mithila, Maliha Tabassum |
| title |
U-net Based Autonomous Fetal Segmentation From 2D and 3D Ultrasound Images |
| title_short |
U-net Based Autonomous Fetal Segmentation From 2D and 3D Ultrasound Images |
| title_full |
U-net Based Autonomous Fetal Segmentation From 2D and 3D Ultrasound Images |
| title_fullStr |
U-net Based Autonomous Fetal Segmentation From 2D and 3D Ultrasound Images |
| title_full_unstemmed |
U-net Based Autonomous Fetal Segmentation From 2D and 3D Ultrasound Images |
| title_sort |
u-net based autonomous fetal segmentation from 2d and 3d ultrasound images |
| publisher |
Brac University |
| publishDate |
2022 |
| url |
http://hdl.handle.net/10361/17647 |
| work_keys_str_mv |
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| _version_ |
1814307356480110592 |