Dr. Jin Huang Profile

Dr. Jin Huang

at Wuhan Textile Univ

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SPIE Journal Paper | 23 June 2023 Open Access

DDeep3M+: adaptive enhancement powered weakly supervised learning for neuron segmentation

Rong Xiao, Lei Zhu, Jiangshan Liao, Xinglong Wu, Hui Gong, Jin Huang, Ping Li, Bin Sheng, Shangbin Chen

NPh, Vol. 10, Issue 03, 035003, (June 2023) https://doi.org/10.1117/12.10.1117/1.NPh.10.3.035003

KEYWORDS: Image segmentation, Neurons, Education and training, Machine learning, Voxels, Image enhancement, 3D image processing, Deep learning, Matrices, Tunable filters

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SignificanceRobust segmentations of neurons greatly improve neuronal population reconstruction, which could support further study of neuron morphology for brain research.AimPrecise segmentation of 3D neuron structures from optical microscopy (OM) images is crucial to probe neural circuits and brain functions. However, the high noise and low contrast of images make neuron segmentation challenging. Convolutional neural networks (CNNs) can provide feasible solutions for the task but they require large manual labels for training. Labor-intensive labeling is highly expensive and heavily limits the algorithm generalization.ApproachWe devise a weakly supervised learning framework Docker-based deep network plus (DDeep3M+) for neuron segmentation without any manual labeling. A Hessian analysis based adaptive enhancement filter is employed to generate pseudo-labels for segmenting neuron images. The automated segmentation labels are input for training a DDeep3M to extract neuronal features. We mine more undetected weak neurites from the probability map based on neuronal structures, thereby modifying the pseudo-labels. We iteratively refine the pseudo-labels and retrain the DDeep3M model with the pseudo-labels to obtain a final segmentation result.ResultsThe proposed method achieves promising results with the F1 score of 0.973, which is close to that of the CNN model with manual labels and superior to several segmentation algorithms.ConclusionsWe propose an accurate weakly supervised neuron segmentation method. The high precision results achieved on 3D OM datasets demonstrate the superior generalization of our DDeep3M+.

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