id:"swepub:oai:research.chalmers.se:d5fba674-d007-4c90-96fa-16d3e0a42f53"
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| Fältnamn | Indikatorer | Metadata |
|---|---|---|
| 000 | 03514nam a2200397 4500 | |
| 001 | oai:research.chalmers.se:d5fba674-d007-4c90-96fa-16d3e0a42f53 | |
| 003 | SwePub | |
| 008 | 191210s2020 | |||||||||||000 ||eng| | |
| 020 | a 9789179052348 | |
| 024 | 7 | a https://research.chalmers.se/publication/5142242 URI |
| 040 | a (SwePub)cth | |
| 041 | a engb eng | |
| 042 | 9 SwePub | |
| 072 | 7 | a dok2 swepub-publicationtype |
| 072 | 7 | a vet2 swepub-contenttype |
| 100 | 1 | a Alvén, Jennifer,d 1989u Chalmers tekniska högskola,Chalmers University of Technology4 aut0 (Swepub:cth)alven |
| 245 | 1 0 | a Combining Shape and Learning for Medical Image Analysis |
| 264 | 1 | a Gothenburg,c 2020 |
| 338 | a electronic2 rdacarrier | |
| 520 | a Automatic methods with the ability to make accurate, fast and robust assessments of medical images are highly requested in medical research and clinical care. Excellent automatic algorithms are characterized by speed, allowing for scalability, and an accuracy comparable to an expert radiologist. They should produce morphologically and physiologically plausible results while generalizing well to unseen and rare anatomies. Still, there are few, if any, applications where today's automatic methods succeed to meet these requirements. The focus of this thesis is two tasks essential for enabling automatic medical image assessment, medical image segmentation and medical image registration . Medical image registration, i.e. aligning two separate medical images, is used as an important sub-routine in many image analysis tools as well as in image fusion, disease progress tracking and population statistics. Medical image segmentation, i.e. delineating anatomically or physiologically meaningful boundaries, is used for both diagnostic and visualization purposes in a wide range of applications, e.g. in computer-aided diagnosis and surgery. The thesis comprises five papers addressing medical image registration and/or segmentation for a diverse set of applications and modalities, i.e. pericardium segmentation in cardiac CTA, brain region parcellation in MRI, multi-organ segmentation in CT, heart ventricle segmentation in cardiac ultrasound and tau PET registration. The five papers propose competitive registration and segmentation methods enabled by machine learning techniques, e.g. random decision forests and convolutional neural networks, as well as by shape modelling, e.g. multi-atlas segmentation and conditional random fields. | |
| 650 | 7 | a NATURVETENSKAPx Data- och informationsvetenskapx Datorseende och robotik0 (SwePub)102072 hsv//swe |
| 650 | 7 | a NATURAL SCIENCESx Computer and Information Sciencesx Computer Vision and Robotics0 (SwePub)102072 hsv//eng |
| 650 | 7 | a TEKNIK OCH TEKNOLOGIERx Medicinteknikx Medicinsk bildbehandling0 (SwePub)206032 hsv//swe |
| 650 | 7 | a ENGINEERING AND TECHNOLOGYx Medical Engineeringx Medical Image Processing0 (SwePub)206032 hsv//eng |
| 653 | a feature-based registration | |
| 653 | a convolutional neural networks | |
| 653 | a conditional random fields | |
| 653 | a medical image segmentation | |
| 653 | a random decision forests | |
| 653 | a machine learning | |
| 653 | a multi-atlas segmentation | |
| 653 | a medical image registration | |
| 653 | a shape models | |
| 710 | 2 | a Chalmers tekniska högskola4 org |
| 856 | 4 | u https://research.chalmers.se/publication/514224/file/514224_Fulltext.pdfx primaryx freey FULLTEXT |
| 856 | 4 8 | u https://research.chalmers.se/publication/514224 |
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