Search: WFRF:(Ulén Johannes)
> (2015-2019) >
Deep learning for s...
Deep learning for segmentation of 49 selected bones in CT scans: First step in automated PET/CT-based 3D quantification of skeletal metastases
-
- Lindgren Belal, Sarah (author)
- Lund University,Lunds universitet,Nuklearmedicin, Malmö,Forskargrupper vid Lunds universitet,Nuclear medicine, Malmö,Lund University Research Groups
-
- Sadik, M. (author)
- Sahlgrenska universitetssjukhuset,Sahlgrenska University Hospital
-
- Kaboteh, R. (author)
- Sahlgrenska universitetssjukhuset,Sahlgrenska University Hospital
-
show more...
-
- Enqvist, Olof, 1981 (author)
- Chalmers University of Technology
-
- Ulén, Johannes (author)
- Eigenvision AB
-
- Poulsen, M. H. (author)
- Odense University Hospital
-
- Simonsen, J. (author)
- Odense University Hospital
-
- Hoilund-Carlsen, P. F. (author)
- Odense University Hospital
-
- Edenbrandt, Lars (author)
- Sahlgrenska universitetssjukhuset,Sahlgrenska University Hospital
-
- Trägårdh, Elin (author)
- Lund University,Lunds universitet,Nuklearmedicin, Malmö,Forskargrupper vid Lunds universitet,WCMM- Wallenberg center för molekylär medicinsk forskning,Medicinska fakulteten,Nuclear medicine, Malmö,Lund University Research Groups,WCMM-Wallenberg Centre for Molecular Medicine,Faculty of Medicine
-
show less...
-
(creator_code:org_t)
- Elsevier BV, 2019
- 2019
- English.
-
In: European Journal of Radiology. - : Elsevier BV. - 0720-048X .- 1872-7727. ; 113, s. 89-95
- Related links:
-
https://research.cha... (primary) (free)
-
show more...
-
http://www.ejradiolo...
-
http://dx.doi.org/10... (free)
-
https://doi.org/10.1...
-
https://research.cha...
-
https://lup.lub.lu.s...
-
show less...
Abstract
Subject headings
Close
- Purpose: The aim of this study was to develop a deep learning-based method for segmentation of bones in CT scans and test its accuracy compared to manual delineation, as a first step in the creation of an automated PET/CT-based method for quantifying skeletal tumour burden. Methods: Convolutional neural networks (CNNs) were trained to segment 49 bones using manual segmentations from 100 CT scans. After training, the CNN-based segmentation method was tested on 46 patients with prostate cancer, who had undergone 18 F-choline-PET/CT and 18 F-NaF PET/CT less than three weeks apart. Bone volumes were calculated from the segmentations. The network's performance was compared with manual segmentations of five bones made by an experienced physician. Accuracy of the spatial overlap between automated CNN-based and manual segmentations of these five bones was assessed using the Sørensen-Dice index (SDI). Reproducibility was evaluated applying the Bland-Altman method. Results: The median (SD) volumes of the five selected bones were by CNN and manual segmentation: Th7 41 (3.8) and 36 (5.1), L3 76 (13) and 75 (9.2), sacrum 284 (40) and 283 (26), 7th rib 33 (3.9) and 31 (4.8), sternum 80 (11) and 72 (9.2), respectively. Median SDIs were 0.86 (Th7), 0.85 (L3), 0.88 (sacrum), 0.84 (7th rib) and 0.83 (sternum). The intraobserver volume difference was less with CNN-based than manual approach: Th7 2% and 14%, L3 7% and 8%, sacrum 1% and 3%, 7th rib 1% and 6%, sternum 3% and 5%, respectively. The average volume difference measured as ratio volume difference/mean volume between the two CNN-based segmentations was 5–6% for the vertebral column and ribs and ≤3% for other bones. Conclusion: The new deep learning-based method for automated segmentation of bones in CT scans provided highly accurate bone volumes in a fast and automated way and, thus, appears to be a valuable first step in the development of a clinical useful processing procedure providing reliable skeletal segmentation as a key part of quantification of skeletal metastases.
Subject headings
- MEDICIN OCH HÄLSOVETENSKAP -- Klinisk medicin -- Ortopedi (hsv//swe)
- MEDICAL AND HEALTH SCIENCES -- Clinical Medicine -- Orthopaedics (hsv//eng)
- MEDICIN OCH HÄLSOVETENSKAP -- Klinisk medicin -- Radiologi och bildbehandling (hsv//swe)
- MEDICAL AND HEALTH SCIENCES -- Clinical Medicine -- Radiology, Nuclear Medicine and Medical Imaging (hsv//eng)
- TEKNIK OCH TEKNOLOGIER -- Medicinteknik -- Medicinsk bildbehandling (hsv//swe)
- ENGINEERING AND TECHNOLOGY -- Medical Engineering -- Medical Image Processing (hsv//eng)
- MEDICIN OCH HÄLSOVETENSKAP -- Klinisk medicin -- Cancer och onkologi (hsv//swe)
- MEDICAL AND HEALTH SCIENCES -- Clinical Medicine -- Cancer and Oncology (hsv//eng)
Keyword
- PET/CT
- Prostate cancer
- Artificial intelligence
- Metastases
- Bone
- Artificial intelligence
- Bone
- Metastases
- PET/CT
- Prostate cancer
Publication and Content Type
- art (subject category)
- ref (subject category)
Find in a library
To the university's database
- By the author/editor
-
Lindgren Belal, ...
-
Sadik, M.
-
Kaboteh, R.
-
Enqvist, Olof, 1 ...
-
Ulén, Johannes
-
Poulsen, M. H.
-
show more...
-
Simonsen, J.
-
Hoilund-Carlsen, ...
-
Edenbrandt, Lars
-
Trägårdh, Elin
-
show less...
- About the subject
-
- MEDICAL AND HEALTH SCIENCES
-
MEDICAL AND HEAL ...
-
and Clinical Medicin ...
-
and Orthopaedics
-
- MEDICAL AND HEALTH SCIENCES
-
MEDICAL AND HEAL ...
-
and Clinical Medicin ...
-
and Radiology Nuclea ...
-
- ENGINEERING AND TECHNOLOGY
-
ENGINEERING AND ...
-
and Medical Engineer ...
-
and Medical Image Pr ...
-
- MEDICAL AND HEALTH SCIENCES
-
MEDICAL AND HEAL ...
-
and Clinical Medicin ...
-
and Cancer and Oncol ...
- Articles in the publication
-
European Journal ...
- By the university
-
Chalmers University of Technology
-
Lund University