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Exploring Fusion Models in Computer ...

State University of New York at Buffalo., Computer Science and Engineering.

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Exploring Fusion Models in Computer Vision for Medical Image Computing.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Exploring Fusion Models in Computer Vision for Medical Image Computing./
作者:	Sarikaya, Duygu.
出版者:	Ann Arbor : ProQuest Dissertations & Theses, : 2017,
面頁冊數:	177 p.
附註:	Source: Dissertation Abstracts International, Volume: 78-11(E), Section: B.
Contained By:	Dissertation Abstracts International78-11B(E).
標題:	Computer science. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=10284723
ISBN:	9780355048100

Exploring Fusion Models in Computer Vision for Medical Image Computing.
Sarikaya, Duygu.

Exploring Fusion Models in Computer Vision for Medical Image Computing. - Ann Arbor : ProQuest Dissertations & Theses, 2017 - 177 p.

Source: Dissertation Abstracts International, Volume: 78-11(E), Section: B.

Thesis (Ph.D.)--State University of New York at Buffalo, 2017.

This item is not available from ProQuest Dissertations & Theses.

Automatic understanding of medical images has been an active research area, and universally-accepted, standard solutions are high in demand. The recent advances of computer vision and machine learning algorithms coupled with the use of digital imaging modalities facilitating efficient storage and access, has led to the improvements in automatic understanding of medical imaging data. Scalable and efficient computer vision algorithms are used in medicine for a broad range of open research problems such as diagnostics, image-guided therapy, automation, and augmented reality for surgical planning and navigation. Intelligent designs and algorithms answer to the needs of medicine by addressing the time, cost, and expertise concerns while hoping to create objective, universally-accepted, standardized, and validated metrics for medical imaging.

ISBN: 9780355048100Subjects--Topical Terms:

523869
Computer science.

Exploring Fusion Models in Computer Vision for Medical Image Computing.
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245 1 0 $a Exploring Fusion Models in Computer Vision for Medical Image Computing.
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300 $a 177 p.
500 $a Source: Dissertation Abstracts International, Volume: 78-11(E), Section: B.
500 $a Adviser: Jason J. Corso.
502 $a Thesis (Ph.D.)--State University of New York at Buffalo, 2017.
506 $a This item is not available from ProQuest Dissertations & Theses.
520 $a Automatic understanding of medical images has been an active research area, and universally-accepted, standard solutions are high in demand. The recent advances of computer vision and machine learning algorithms coupled with the use of digital imaging modalities facilitating efficient storage and access, has led to the improvements in automatic understanding of medical imaging data. Scalable and efficient computer vision algorithms are used in medicine for a broad range of open research problems such as diagnostics, image-guided therapy, automation, and augmented reality for surgical planning and navigation. Intelligent designs and algorithms answer to the needs of medicine by addressing the time, cost, and expertise concerns while hoping to create objective, universally-accepted, standardized, and validated metrics for medical imaging.
520 $a In this thesis, we propose solutions to the open problems of robot-assisted surgery (RAS) video understanding, and segmentation of the tumor and anatomical structures of the brain. Video understanding of robot-assisted surgery (RAS) videos is an open problem in the computer vision and medical communities. Modeling the gestures and skill level of surgeons presents an interesting problem. Early identification of technical competence in surgical skills is expected to help tailor training to personalized needs of surgeons in training. The insights drawn may be applied in effective skill acquisition, objective skill assessment, real-time feedback, and human-robot collaborative surgeries. Characterization of anatomical structure of the brain in magnetic resonance human brain images (MRI) has also gained high interest in recent years. There is a strong need for a computer-aided system that will automatically and accurately define the tumor and anatomical structures of the brain. A universally-accepted, standardized automatic mapping and segmentation of brain volumetry would be a significant improvement for diagnosis, modeling personalized progression, and treatment monitoring of neurologic conditions.
520 $a Many recent studies has shown that exploiting the relationship across different tasks, jointly reasoning multiple tasks and taking advantage of a combination of shared and task-specific representations is a good idea and many end-to-end multi-task models perform astonishingly better than their single-task counterparts. What stands out in this thesis is the facilitation of information fusion whether it is multiple modalities combining shared and task-specific representations, joint relationships or using smart fusion of a priori knowledge inferred from multiple atlases. Our studies show that using an appropriate fusion approach increases the accuracy of these tasks and performs better than approaches without fusion.
520 $a We first propose an end-to-end solution to the tool detection and localization open problem in robot-assisted surgery (RAS) video understanding, using a strictly computer vision approach and the recent advances of deep learning. We propose a novel architecture using multimodal convolutional neural networks for fast detection and localization of tools in RAS videos. Our architecture applies a Region Proposal Network (RPN), and a multi-modal two stream convolutional network for object detection, to jointly predict objectness and localization on a late fusion of image and temporal motion cues. We also introduce and publicly release a new dataset, ATLAS Dione, for RAS video understanding. Our dataset provides video data (86 full subject task study videos, 910 subtask clips with a total of 5 hours) of ten surgeons from Roswell Park Cancer Institute (RPCI) (Buffalo, NY) performing six different surgical tasks on the daVinci Surgical System (dVSSRTM) with annotations of robotic tools, timestamps of surgical actions, and expertise levels of the surgeons.
520 $a We then propose a novel architecture for activity recognition in RAS videos. Our end-to-end architecture is based on the principles of recurrent neural network that jointly learns temporal dynamics and visual features by convolutional network models. We argue that, surgical tasks are better modeled by the visual features that are determined by the objects in the scene, while the gestures, which are small activity segments that reoccur across multiple surgical tasks, are better modeled with motion cues. However, we also believe that visual features are complementary to the motion cues which are independent of object and scene features. We propose an architecture that jointly learns multiple tasks on the two modalities of the input video: visual features and the flow information relating the motion cues. Our architecture simultaneously recognizes the gestures and classifies them under tasks by making use of these joint relationships, combining shared and task-specific representations to achieve better performance.
520 $a For the segmentation of the anatomical structures of the brain, we use a priori information of multi-atlases based on the intensity similarity information of voxels as well as the spatial correspondence to infer knowledge, and then we segment brain MRI data with a multiway cut algorithm.
520 $a For the segmentation of tumor in brain MRI, we define 3D joint histograms that are representative of each subject MRI in three different modalities. (Abstract shortened by ProQuest.).
590 $a School code: 0656.
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650 4 $a Medical imaging. $3 3172799
650 4 $a Artificial intelligence. $3 516317
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710 2 $a State University of New York at Buffalo. $b Computer Science and Engineering. $3 1035503
773 0 $t Dissertation Abstracts International $g 78-11B(E).
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791 $a Ph.D.
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793 $a English
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