Automatic left ventricular myocardium segmentation and volumetric classification of scar (Record no. 6567)
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| 000 -LEADER | |
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| fixed length control field | 01890nam a22002057a 4500 |
| 003 - CONTROL NUMBER IDENTIFIER | |
| control field | OSt |
| 005 - DATE AND TIME OF LATEST TRANSACTION | |
| control field | 20220107122853.0 |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION | |
| fixed length control field | 190710b xxu||||| |||| 00| 0 eng d |
| 040 ## - CATALOGING SOURCE | |
| Transcribing agency | IIITMK |
| 100 ## - MAIN ENTRY--PERSONAL NAME | |
| Personal name | Jayadeep (41718004) |
| 9 (RLIN) | 16160 |
| 245 ## - TITLE STATEMENT | |
| Title | Automatic left ventricular myocardium segmentation and volumetric classification of scar |
| 300 ## - PHYSICAL DESCRIPTION | |
| Extent | MPhil CS 2018-2019 |
| 500 ## - GENERAL NOTE | |
| General note | Cardiac function is of predominant importance for both prognosis and treatment<br/>of different pathologies such as mitral regurgitation,ischmia,dyssynchrony and<br/>myocarditis. Cardiac behaviour is determined by structural and functional<br/>features. In both cases, the analysis of medical imaging studies requires to detect<br/>and segment the myocardium. Nowadays, magnetic resonance imaging is one of<br/>the most relevant and accurate non-invasive diagnostic tools for cardiac structure<br/>and function. In this, we present deep learning technique stack auto encoder<br/>architecture to segment images from a series of short-axis cardiac magnetic<br/>resonance slices, we propose to tackle the problem of automated left ventricle<br/>segmentation through the application of deep learning technique.It takes as input<br/>raw magnetic resonance imaging images,requires no manual pre processing or<br/>image cropping and is trained to segment the endocardium of the left ventricle,<br/>as well as the center of the left ventricle. The quantitative evaluation is based on<br/>three commaon metrics which are accuracy, dice and sensitivity respectively and<br/>classifciation using random forest classifier. The classifciation is to be done on<br/>the image set to determine whether the myocardium is viable or normal. |
| 502 ## - DISSERTATION NOTE | |
| Degree type | MPhil CS |
| Name of granting institution | 2018-2019 |
| Year degree granted | INT |
| -- | Dr Elizabeth Sherly |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical term or geographic name entry element | VENTRICULAR MYOCARDIUM |
| 9 (RLIN) | 16161 |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical term or geographic name entry element | CARDIAC MRI |
| 9 (RLIN) | 16162 |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical term or geographic name entry element | CMR IMAGES |
| 9 (RLIN) | 16163 |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical term or geographic name entry element | CONVOLUTIONAL NEURAL NETWORK |
| 9 (RLIN) | 16164 |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) | |
| Source of classification or shelving scheme | Dewey Decimal Classification |
| Koha item type | |
| Withdrawn status | Lost status | Source of classification or shelving scheme | Damaged status | Not for loan | Collection code | Home library | Current library | Shelving location | Date acquired | Total Checkouts | Barcode | Date last seen | Price effective from | Koha item type |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Dewey Decimal Classification | Non Fiction | IIITM-K | Kerala University of Digital Sciences, Innovation and Technology Knowledge Centre | 10/07/2019 | R-1571 | 10/07/2019 | 10/07/2019 | Project Reports |