Global ETD Search
Search theses and dissertations gathered from participating repositories worldwide. Every result links back to the library that holds it. No account is needed.
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Showing 1 to 20 of 32 for “"Medical Image Segmentation"”.
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Semi-automatic medical image segmentation
Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, February 2002.
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Text Prompt-Driven Medical Image Segmentation
Medical image segmentation plays a crucial role in accurate diagnosis, treatment planning, and surgical navigation by precisely identifying pathological regions. However, traditional segmentation methods typically rely on dense pixel-level annotations and heavy computational resources, which pose …
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Intercomparison of medical image segmentation algorithms
… are several stages involved in analyzing an MRI image, segmentation being one of the most important. Image segmentation is essentially the process of identifying and classifying the constituent parts of an image, and is usually very complex. Unfortunately, it suffers from artefacts including …
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Curve evolution for medical image segmentation
Thesis (Ph.D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2000.
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Machine Learning towards General Medical Image Segmentation
… is proportionate to a physician's workload. Segmentation is a fundamental limiting precursor to diagnostic and therapeutic procedures. Advances in machine learning aims to increase diagnostic efficiency to replace single applications with generalized algorithms. We approached segmentation as …
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Model based three dimensional medical image segmentation
Thesis (Ph.D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 1999.
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Learning with imperfect datasets in medical image segmentation
Medical image segmentation partitions medical images into distinct physiological regions, such as organs and lesions, essential for diagnosis and treatment planning. Deep neural networks have advanced this field recently, yet real-world performance remains unsatisfactory due to imperfect data and …
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Intranet: infrared-based transformers for 2D medical image segmentation
Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2023-09-01 without embargo terms
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Development of supervised and unsupervised pixel-based classification methods for medical image segmentation
… present thesis are: (i) to develop a reliable segmentation methodology for detection of ER-expressed nuclei in breast cancer tissue images stained with IHC, (ii) to objectively quantify ER status in breast cancer tissue images stained with IHC, (iii) to investigate potential correlation between …
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Transforming Medical Image Segmentation with Enhanced U-Net Architectures and Adaptive Transfer Learning
Medical imaging has revolutionized healthcare by enabling accurate diagnosis, treatment planning, and monitoring of various diseases. Various modalities, such as computed tomography (CT), magnetic resonance imaging (MRI), and ultrasound, visualize diverse anatomical structures and pathological …
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SHAPE INFLUENCE IN MEDICAL IMAGE SEGMENTATION WITH APPLICATION IN COMPUTER AIDED DIAGNOSIS IN CT COLONOGRAPHY
… or physicians in the interpretation of medical images. The application of CAD in screening colorectal cancer (CRC) has been studied for more than two decades. CRC is the second most deadly form of cancer in men and women in the United States. Nearly all CRC arises from polyps and is …
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Three-Dimensional Modeling and Finite Element Analysis of the Human Diaphragm
… Element Analysis (FEA). ITK-SNAP was used for medical image segmentation, CATIA V5 for 3D reconstruction, and ANSYS for simulation under various pressure scenarios. The reconstructed diaphragm model was validated against anatomical landmarks and literature-based deformation ranges, showing good …
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Learning to Segment Unseen Tasks In-Context
… models have become the predominant method for medical image segmentation, they are typically incapable of generalizing to new segmentation tasks---involving new anatomies, image modalities, or labels. For a new segmentation task, researchers will often have to prepare new task-specific models. …
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Efficient Segment Anything on the Edge
… model facilitating promptable and zero-shot image segmentation. SAM-based models have a wide range of applications including autonomous driving, medical image segmentation, VR, and data annotation. However, SAM models are highly computationally intensive and lack a flexible prompting …
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Deep Learning Methods for Muscle Analysis From Magnetic Resonance Images
… to muscular diseases in medicine, to name a few. Medical imaging offers noninvasive tools for muscle size and structure analysis, typically necessitating pixel/voxel-wise annotations. Segmentation frameworks utilising deep learning offer a data-driven approach to automate the annotation process …
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Learning distributions of transformations from small datasets for applied image synthesis
… this thesis, we investigate two applications of image synthesis using small datasets. First, we demonstrate how to use image synthesis to perform data augmentation, enabling the use of supervised learning methods with limited labeled data. Data augmentation -- typically the application of simple, …
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Towards Resilient Models: A Deep Learning Odyssey through Mammographic Images
… and accurate models tailored specifically for medical imaging tasks. This thesis explores the complexities of this domain by addressing two pivotal aspects: mammographic image classification and segmentation. In contrast to conventional single-view analyses, this approach recognizes and …
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Segmentation of cervical and lumbar vertebrae in x-ray images using active appearance models and extensions
This thesis presents a hierarchical segmentation algorithm tailored to the segmentation of cervical and lumbar vertebrae in digitized X-ray images. The algorithm employs the Generalized Hough Transform (GHT) to obtain a suitable initialization for two segmentation stages that utilize Active …
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Nature-based algorithms for deep learning based systems and applications.
… on the application of NBA-optimised DLBS for medical image segmentation, we made two major contributions. We developed a weighted ensemble framework where optimal weights for combining predictions from diverse deep segmentation models are determined efficiently using NBA, maximising the Dice …
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3D multiresolution statistical approaches for accelerated medical image and volume segmentation
Medical volume segmentation got the attraction of many researchers; therefore, many techniques have been implemented in terms of medical imaging including segmentations and other imaging processes. This research focuses on an implementation of segmentation system which uses several techniques …
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