뉴런 추적 및 딥러닝 기반의 대화형 뉴런 구조 교정 기법
Neuron Tracing- and Deep Learning-guided Interactive Proofreading for Neuron Structure Segmentation
0
조회
0
다운로드
0
인용
0
참고문헌
키워드
초록
축삭(axon), 가지돌기(dendrite), 신경세포체(cell body)와 같은 뉴런의 소기관을 분리하는 작업은 신경학적 현상의 분석에 도움을 준다. 최근에 딥러닝 기술을 이용하여 이를 수행하고자 하는 시도들이 있지만, 데이터의 노이즈, 훈련 데이터와의 차이등으로 인해 결과에 오류를 포함할 가능성이 있다. 따라서, 이러한 기술을 실제 분석에 활용하기 위해서는 결과를 교정하는과정이 필수적이지만, 이는 전문가가 수작업으로 수행해야 하기 때문에 많은 노력과 시간이 소요된다. 우리는 딥러닝 결과에존재하는 오류들을 보다 손쉽게 교정할 수 있는 대화형 뉴런 구조 교정 방법을 제안한다. 이 방법은 구조적 일관성을 지니는뉴런의 특성을 기반으로 뉴런 구조를 교정하여 적은 사용자의 인터랙션으로도 높은 정확도의 교정 결과를 얻을 수 있도록한다.
Abstract
Segmenting the compartments of neurons, such as axons, dendrites, and cell bodies, is helpful in the analysis of neurological phenomena. Recently, there have been several studies to segment the compartments through deep learning. However, this approach has the potential to include errors in the results due to noise in data and differences between training data and actual data. Therefore, in order to use these for actual analysis, it is essential to proofread the results. The proofreading process requires a lot of effort and time because an expert must perform it manually. We propose an interactive neuron structure proofreading method that can more easily correct errors in the segmentation results of a deep learning. This method proofread the neuron structure based on the characteristics of the neuron with structural consistency, so that a high-accuracy proofreading result can be obtained with less interaction.
참고문헌 (16)
- M. London, "Dendritic computation", Annu. Rev. Neurosci., 28, pp.503~532 (2005)
- T. L. Lewis, "Mff-dependent mitochondrial fission regulates presynaptic release and axon branching by limiting axonal mitochondria size", Nature communications, 9(1), pp.1~15 (2018)
- C. Park, "Neuron segmentation using incomplete and noisy labels via adaptive learning with structure priors", 2021IEEE 18th International Symposium on Biomedical Imaging (ISBI 2021) (2021)
- N. Papernot, "The limitations of deep learning in adversarial settings", 2016 IEEE European symposium on security and privacy (EuroS&P), pp.372~387 (2016)
- D. Karimi, "Deep learning with noisy labels : Exploring techniques and remedies in medical image analysis", Medical Image Analysis, 65, pp.101759~ (2020)
- A. Mosinska, "Active learning and proofreading for delineation of curvilinear structures", International Conference on Medical Image Computing and Computer-Assisted Intervention, pp.165~173 (2017)
- V. J. Dercksen, "The filament editor : an interactive software environment for visualization, proof-editing and analysis of 3d neuron morphology", Neuroinformatics, 12(2), pp.325~339 (2014)
- L. Acciai, "Automated neuron tracing methods : an updated account", Neuroinformatics, 14(4), pp.353~367 (2016)
- H. Peng, "Bigneuron : large-scale 3d neuron reconstruction from optical microscopy images", Neuron, 87(2), pp.252~256 (2015)
- R. Lin, "Cell-type-specific and projection-specific brain-wide reconstruction of single neurons", Nature methods, 15(12), pp.1033~1036 (2018)
- N. Krasowski, "Neuron segmentation with highlevel biological priors", IEEE transactions on medical imaging, 37(4), pp.829~839 (2017)
- A. B. Schroeder, "The imagej ecosystem : Opensource software for image visualization, processing, and analysis", Protein Science, 30(1), pp.234~249 (2021)
- P. A. Yushkevich, "Itk-snap : an intractive medical image segmentation tool to meet the need for expertguided segmentation of complex medical images", IEEE pulse, 8(4), pp.54~57 (2017)
- Q. Huang, "Weakly supervised learning of 3d deep network for neuron reconstruction", Frontiers in Neuroanatomy, 14 (2020)
- Y. Deng, "Fuzzy dijkstra algorithm for shortest path problem under uncertain environment", Applied Soft Computing, 12(3), pp.1231~1237 (2012)
- D. P. Kingma, "Adam : A method for stochastic optimization", arXiv preprint arXiv : 1412. 6980, ~ (2014)