MEDICAL IMAGE SEGMENTATION FOR EMBRYO IMAGE ANALYSIS

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dc.contributor.advisorOhta, Aaron
dc.contributor.advisorChun, Il Yong
dc.contributor.authorHarun, Md Yousuf
dc.contributor.departmentElectrical Engineering
dc.date.accessioned2020-07-07T19:12:45Z
dc.date.available2020-07-07T19:12:45Z
dc.date.issued2020
dc.description.degreeM.S.
dc.identifier.urihttp://hdl.handle.net/10125/68984
dc.subjectElectrical engineering
dc.titleMEDICAL IMAGE SEGMENTATION FOR EMBRYO IMAGE ANALYSIS
dc.typeThesis
dcterms.abstractThis thesis describes a project that applies electrical engineering to biomedical applications. The project involves the development of a deep learning-based image segmentation method to identify cellular regions in microscopic images of human embryos for their morphological and morphokinetic analysis during in vitro fertilization (IVF) treatment. First, we aim to segment inner cell mass (ICM) and trophectoderm epithelium (TE) in zona pellucida (ZP)-intact embryos imaged by a microscope for morphological analysis. ICM and TE segmentation in ZP-intact embryonic images is difficult due to small number of training images (211 ZP-intact embryonic images) and similar textures among ICM, TE, ZP, and artifacts. We overcame the aforementioned challenges by leveraging deep learning and semantic segmentation techniques. In this work, we implemented a UNet variant model named Residual Dilated UNet (RD-UNet) to segment ICM and TE in ZP-intact embryonic images. We added residual convolution to the encoding and decoding units and replaced conventional convolutional layer with multiple dilated convolutional layers at the central bridge of RD-UNet. The experimental results with a testing set of 38 ZP-intact embryonic images demonstrate that RD-UNet outperforms existing models. RD-UNet can identify ICM with a Dice Coefficient of 94.3% and a Jaccard Index of 89.3%. The model can segment TE with a Dice Coefficient of 92.5% and a Jaccard Index of 85.3%. Second, we aim to segment inner cell regions in ZP-ablated embryonic images obtained by time-lapse microscopic imaging for morphokinetic analysis. Segmenting inner cell regions in ZP-ablated embryonic images has following challenges: irregular expansion of inner cell, surrounding fragmented cellular clusters and artifacts, and inner cell expansion beyond culture well. We proposed a UNet based architecture named Deep Dilated Residual Recurrent UNet (D2R2-UNet) to segment inner cell regions in ZP-ablated embryonic images. We incorporated residual recurrent convolution into the encoding and decoding units, dilated convolution into the central bridge, and residual convolution into the encoder-decoder skip-connections in order to maximize the segmentation performance. The experimental results with a testing set of 342 ZP-ablated embryonic images demonstrate that the proposed D2R2-UNet improves inner cell segmentation performances over existing UNet variants. Our model obtains the best overall performance as compared to other models in inner cell segmentation, with a Jaccard Index of 95.65% and a Dice Coefficient of 97.78%.
dcterms.extent56 pages
dcterms.languageeng
dcterms.publisherUniversity of Hawai'i at Manoa
dcterms.rightsAll UHM dissertations and 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 from the copyright owner.
dcterms.typeText
local.identifier.alturihttp://dissertations.umi.com/hawii:10680

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M.S. - Electrical Engineering