Evaluation of multislice inputs to convolutional neural networks for medical image segmentation. Issue 12 (10th November 2020)

• Record Type: Journal Article
• Authors: Vu, Minh H.; Grimbergen, Guus; Nyholm, Tufve; Löfstedt, Tommy
• Is Part Of: Medical physics. Volume 47:Issue 12(2020)
• Page Start: 6216
• Page End: 6231
• Publication Date: 2020-11-10
• Subjects: convolutional neural network -- deep learning -- medical image segmentation -- multislice; Medical physics -- Periodicals; Medical physics; Geneeskunde; Natuurkunde; Toepassingen; Biophysics; Periodicals; Periodicals; Electronic journals; 610.153
• DOI: 10.1002/mp.14391
• Languages: English
• ISSNs: 0094-2405

Abstract

Abstract : Purpose: When using convolutional neural networks (CNNs) for segmentation of organs and lesions in medical images, the conventional approach is to work with inputs and outputs either as single slice [two‐dimensional (2D)] or whole volumes [three‐dimensional (3D)]. One common alternative, in this study denoted as pseudo‐3D, is to use a stack of adjacent slices as input and produce a prediction for at least the central slice. This approach gives the network the possibility to capture 3D spatial information, with only a minor additional computational cost. Methods: In this study, we systematically evaluate the segmentation performance and computational costs of this pseudo‐3D approach as a function of the number of input slices, and compare the results to conventional end‐to‐end 2D and 3D CNNs, and to triplanar orthogonal 2D CNNs. The standard pseudo‐3D method regards the neighboring slices as multiple input image channels. We additionally design and evaluate a novel, simple approach where the input stack is a volumetric input that is repeatably convolved in 3D to obtain a 2D feature map. This 2D map is in turn fed into a standard 2D network. We conducted experiments using two different CNN backbone architectures and on eight diverse data sets covering different anatomical regions, imaging modalities, and segmentation tasks. Results: We found that while both pseudo‐3D methods can process a large number of slices at once and still be computationally much more efficient than fully 3D CNNs, a significant improvement over a regular 2D CNN was only observed with two of the eight data sets. triplanar networks had the poorest performance of all the evaluated models. An analysis of the structural properties of the segmentation masks revealed no relations to the segmentation performance with respect to the number of input slices. A post hoc rank sum test which combined all metrics and data sets yielded that only our newly proposed pseudo‐3D method with an input size of 13 slices outperformed almost all methods. Conclusion: In the general case, multislice inputs appear not to improve segmentation results over using 2D or 3D CNNs. For the particular case of 13 input slices, the proposed novel pseudo‐3D method does appear to have a slight advantage across all data sets compared to all other methods evaluated in this work.