Deviations from the MR acquisition guidelines could lead to images with serious quality concerns such as incompletely imaged anatomies, which might require re-examinations and could result in missed pathologies. In this paper, we propose a deep learning method to automatically estimate the coverage of the target anatomy and to predict the extent of an anatomy outside the present field-of-view (FOV). For this purpose, we employed a 3D fully-convolutional neural network operating at multiple resolution levels. The proposed solution could be employed to propose a correct FOV setting in case of organ-coverage issues while patient is on the table and could be incorporated as a retrospective tool for quality monitoring and staff training. Our method was evaluated for four abdominal organs - liver, spleen, and left and right kidneys - in 40 magnetic resonance (MR) images from the publicly available Combined Healthy Abdominal Organ Segmentation (CHAOS) dataset. We obtained median extent-detection errors of 5.5-7.3mm or 3-4 voxels in the superior or inferior position in a dataset with average anatomical clippings of 24.8-43.6mm for four partially missing organs in the given FOV.
In order to alleviate the risk of radiation induced cataract in patients undergoing head CT examinations, the guidelines published by the American Association of Physicists in Medicine (AAPM) link the optimal scan angle to particular anatomic landmarks in the skull. In this paper, we investigated the use of a foveal fully-convolutional neural network (F-Net) for the segmentation-based detection of three head CT landmarks, with the final objective of an automatic scan quality control. Three individual networks were trained using ground-truth (GT) from three different readers to investigate the detection accuracy compared to each reader. The experiments were performed using 119 head CT scans and the three-fold cross-validation set up. For the evaluation, two performance measures were employed: the Euclidean distance between the detected landmarks and GT, and the distance of the detected landmarks to the plane generated from the GT landmark positions. For three readers, the median values of the Euclidean and point-to-plane distance obtained using F-Net were in the range of 1.3 - 2.8 mm and 0.3 - 0.8 mm, respectively. The presented method outperformed a previously published approach using image registration and achieved results comparable to the inter-observer variability between three readers. Further improvements were achieved by training a similar network which combined GT information from all readers.
Conference Committee Involvement (7)
Session chair: ISBI
20 March 2022 |
Scientific Reviewer: IEEE Transactions on Medical Imaging (TMI)
1 March 2022 |
Scientific Reviewer: Medical Physics
1 March 2022 |
Scientific Reviewer: MIDL
1 January 2022 |
Scientific Reviewer: ISBI
1 October 2021 |
Scientific Reviewer: MICCAI
1 April 2020 |
Scientific Reviewer: Journal of Computerized Medical Imaging and Graphics (CMIG)
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