Medical Images Segmentation Operations

Extracting various valuable medical information from head MRI and CT series is one of the most important and challenging tasks in the area of medical image analysis. Due to the lack of automation for many of these tasks, they require meticulous preprocessing from the medical experts. Nevertheless, some of these problems may have semi-automatic solutions, but they are still dependent on the person's competence. The main goal of our research project is to create an instrument that maximizes series processing automation degree. Our project consists of two parts: a set of algorithms for medical image processing and tools for its results interpretation. In this paper we present an overview of the best existing approaches in this field, as well the description of our own algorithms developed for similar tissue segmentation problems such as eye bony orbit and brain tumor segmentation based on convolutional neural networks. The investigation of performance of different neural network models for both tasks as well as neural ensembles applied to brain tumor segmentation is presented. We also introduce our software named "MISO Tool" which is created specifically for this type of problems. It allows tissues segmentation using pre-trained neural networks, DICOM pixel data manipulation and 3D reconstruction of segmented areas.

deep neural networks, convolutional neural net-works, brain tumors, bony orbit, medical images, segmentation

1. Wagner M.E., Gellrich N.C., Friese K.I. et al. Model-based segmentation in orbital volume measurement with cone beam computed tomography and evaluation against current concepts. International Journal of Computer Assisted Radiology and Surgery, vol. 11, issue 1, 2016, pp 1–9

2. Jean-Franois D, Andreas B. Atlas-based automatic segmentation of head and neck organs at risk and nodal target volumes: a clinical validation. Radiation Oncology, 2013, 8:154

3. Yushkevich P.A. Piven J., Hazlett H.C. et al. User-guided 3D active contour segmentation of anatomical structures: significantly improved efficiency and reliability. NeuroImage, vol. 31, issue 3, 2006, pp. 1116-1128

4. Doyle, S., Vasseur, F., Dojat, M., Forbes, F. Fully Automatic Brain Tumor Segmentation from Multiple MR Sequences using Hidden Markov Fields and Variational EM. In Procs. of the NCI-MICCAI BRATS, 2013, pp. 18-22

5. Cardoso, M.J., Sudre, C.H., Modat, M., Ourselin, S. Template-based multimodal joint generative model of brain data. Lecture Notes in Computer Science, vol. 9123, 2015, pp. 17-29

6. H. N. Bharath, S. Colleman, D. M. Sima, S. Van Huffel. Tumor Segmentation from Multimodal MRI Using Random Forest with Superpixel and Tensor Based Feature Extraction. Lecture Notes in Computer Science, vol. 10670, 2018, pp. 463-473.

7. Chi-Hoon Lee, Mark Schmidt, Albert Murtha, Aalo Bistritz, Jöerg Sander, Russell Greiner. Segmenting brain tumors with conditional random fields and support vector machines. Lecture Notes in Computer Science, vol. 3765, 2005, pp. 469-478

8. Kamnitsas K., Ledig C., Newcombe V.F.J., Simpson J.P., Kane A.D., Menon D.K., Rueckert D., Glocker B. Efficient multi-scale 3DCNN with fully connected CRF for accurate brain lesion segmentation. Medical Image Analysis, vol. 36, 2017, pp. 61-78.

9. G. Wang, W. Li, S. Ourselin, T. Vercauteren. Automatic brain tumor segmentation using cascaded anisotropic convolutional neural networks. Lecture Notes in Computer Science, vol. 10670, 2018, pp. 178-190

10. Menze B.H., Jakab A., Bauer S. et al. The multimodal brain tumor image segmentation benchmark (BRATS). IEEE Transactions on Medical Imaging, vol. 34, issue 10, 2015, pp. 1993-2024

11. Spyridon Bakas, Hamed Akbari, Aristeidis Sotiras et al. Advancing The Cancer Genome Atlas glioma MRI collections with expert segmentation labels and radiomic features. Scientific Data, vol. 4, 2017, Article number: 170117

12. Günter Klambauer, Thomas Unterthiner, Andreas Mayr, Sepp Hochreiter. Self-Normalizing Neural Networks. Advances in Neural Information Processing Systems, vol. 30, 2017

13. L.K. Hansen and P Salamon. Neural network ensembles. IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 12, issue 10, 1990, pp. 993- 1001

14. J. van Doorn. Analysis of deep convolutional neural network architectures. Available at: https://pdfs.semanticscholar.org/6831/bb247c853b433d7b2b9d47780dc8d84e4762.pdf, accessed: 13.06.2018

15. Hahnloser R.H., Sarpeshkar R., Mahowald M.A., Douglas R.J., Seung H.S. Digital selection and analogue amplification coexist in a cortex-inspired silicon circuit. Nature, vol. 405, 2000, pp. 947–951

16. O. Ronneberger, P. Fischer, and T. Brox. U-Net: Convolutional Networks for Biomedical Image Segmentation. Lecture Notes in Computer Science, vol. 9351, 2015, pp. 234-241

17. Nitish Srivastava, Geoffrey Hinton, Alex Krizhevsky, Ilya Sutskever, and Ruslan Salakhutdinov. Dropout: a simple way to prevent neural networks from overfitting. Journal of Machine Learning Research, vol. 15, issue 1, 2014, pp. 1929-1958