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Simultaneous MR knee image segmentation and bias field correction using deep learning and partial convolution
KTH, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH), Biomedical Engineering and Health Systems. Novamia AB, Uppsala, Sweden.
KTH, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH), Biomedical Engineering and Health Systems, Medical Imaging. Novamia AB, Uppsala, Sweden.ORCID iD: 0000-0002-7750-1917
KTH, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH), Biomedical Engineering and Health Systems, Medical Imaging. Novamia AB, Uppsala, Sweden.ORCID iD: 0000-0002-0442-3524
2019 (English)In: Medical Imaging 2019: Image Processing, SPIE - International Society for Optical Engineering, 2019, Vol. 10949, article id 1094909Conference paper, Published paper (Refereed)
Abstract [en]

Intensity inhomogeneity is a great challenge for automated organ segmentation in magnetic resonance (MR) images. Many segmentation methods fail to deliver satisfactory results when the images are corrupted by a bias field. Although inhomogeneity correction methods exist, they often fail to remove the bias field completely in knee MR images. We present a new iterative approach that simultaneously predicts the segmentation mask of knee structures using a 3D U-net and estimates the bias field in 3D MR knee images using partial convolution operations. First, the test images run through a trained 3D U-net to generate a preliminary segmentation result, which is then fed to the partial convolution filter to create a preliminary estimation of the bias field using the segmented bone mask. Finally, the estimated bias field is then used to produce bias field corrected images as the new inputs to the 3D U-net. Through this loop, the segmentation results and bias field correction are iteratively improved. The proposed method was evaluated on 20 proton-density (PD)-weighted knee MRI scans with manually created segmentation ground truth using 10 fold cross-validation. In our preliminary experiments, the proposed methods outperformed conventional inhomogeneity-correction-plus-segmentation setup in terms of both segmentation accuracy and speed.

Place, publisher, year, edition, pages
SPIE - International Society for Optical Engineering, 2019. Vol. 10949, article id 1094909
Series
Progress in Biomedical Optics and Imaging - Proceedings of SPIE, ISSN 1605-7422 ; 10949
National Category
Medical Image Processing
Identifiers
URN: urn:nbn:se:kth:diva-258892DOI: 10.1117/12.2512950ISI: 000483012700008Scopus ID: 2-s2.0-85068319000ISBN: 9781510625457 (print)OAI: oai:DiVA.org:kth-258892DiVA, id: diva2:1350234
Conference
Medical Imaging 2019: Image Processing; San Diego; United States; 19 February 2019 through 21 February 2019
Note

QC 20190913

Available from: 2019-09-11 Created: 2019-09-11 Last updated: 2019-09-26Bibliographically approved

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Smedby, Örjan

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