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You Z, Jiang M, Shi Z, Zhao M, Shi C, Du S, Hérard AS, Souedet N, Delzescaux T. Multiscale segmentation- and error-guided iterative convolutional neural network for cerebral neuron segmentation in microscopic images. Microsc Res Tech 2022; 85:3541-3552. [PMID: 35855638 DOI: 10.1002/jemt.24206] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/23/2022] [Accepted: 07/07/2022] [Indexed: 11/10/2022]
Abstract
This article uses microscopy images obtained from diverse anatomical regions of macaque brain for neuron semantic segmentation. The complex structure of brain, the large intra-class staining intensity difference within neuron class, the small inter-class staining intensity difference between neuron and tissue class, and the unbalanced dataset increase the difficulty of neuron semantic segmentation. To address this problem, we propose a multiscale segmentation- and error-guided iterative convolutional neural network (MSEG-iCNN) to improve the semantic segmentation performance in major anatomical regions of the macaque brain. After evaluating microscopic images from 17 anatomical regions, the semantic segmentation performance of neurons is improved by 10.6%, 4.0%, 1.5%, and 1.2% compared with Random Forest, FCN-8s, U-Net, and UNet++, respectively. Especially for neurons with brighter staining intensity in the anatomical regions such as lateral geniculate, globus pallidus and hypothalamus, the performance is improved by 66.1%, 23.9%, 11.2%, and 6.7%, respectively. Experiments show that our proposed method can efficiently segment neurons with a wide range of staining intensities. The semantic segmentation results are of great significance and can be further used for neuron instance segmentation, morphological analysis and disease diagnosis. Cell segmentation plays a critical role in extracting cerebral information, such as cell counting, cell morphometry and distribution analysis. Accurate automated neuron segmentation is challenging due to the complex structure of brain, the large intra-class staining intensity difference within neuron class, the small inter-class staining intensity difference between neuron and tissue class, and the unbalanced dataset. The proposed multiscale segmentation- and error-guided iterative convolutional neural network (MSEG-iCNN) improve the segmentation performance in 17 major anatomical regions of the macaque brain.
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Affiliation(s)
- Zhenzhen You
- Shaanxi Key Laboratory for Network Computing and Security Technology, School of Computer Science and Engineering, Xi'an University of Technology, Xi'an, China.,CEA-CNRS-UMR 9199, Laboratoire des Maladies Neurodégénératives, MIRCen, Fontenay-aux-Roses, Université Paris-Saclay, Paris, France
| | - Ming Jiang
- National Laboratory of Radar Signal Processing, Xidian University, Xi'an, China
| | - Zhenghao Shi
- Shaanxi Key Laboratory for Network Computing and Security Technology, School of Computer Science and Engineering, Xi'an University of Technology, Xi'an, China
| | - Minghua Zhao
- Shaanxi Key Laboratory for Network Computing and Security Technology, School of Computer Science and Engineering, Xi'an University of Technology, Xi'an, China
| | - Cheng Shi
- Shaanxi Key Laboratory for Network Computing and Security Technology, School of Computer Science and Engineering, Xi'an University of Technology, Xi'an, China
| | - Shuangli Du
- Shaanxi Key Laboratory for Network Computing and Security Technology, School of Computer Science and Engineering, Xi'an University of Technology, Xi'an, China
| | - Anne-Sophie Hérard
- CEA-CNRS-UMR 9199, Laboratoire des Maladies Neurodégénératives, MIRCen, Fontenay-aux-Roses, Université Paris-Saclay, Paris, France
| | - Nicolas Souedet
- CEA-CNRS-UMR 9199, Laboratoire des Maladies Neurodégénératives, MIRCen, Fontenay-aux-Roses, Université Paris-Saclay, Paris, France
| | - Thierry Delzescaux
- CEA-CNRS-UMR 9199, Laboratoire des Maladies Neurodégénératives, MIRCen, Fontenay-aux-Roses, Université Paris-Saclay, Paris, France
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