[1] FRANGAKIS A S, HEGERL R. Segmentation of two- and three-dimensional data from electron microscopy using eigenvector analysis[J]. Journal of Structural Biology, 138, 105-113(2002).

[2] KYLBERG G, UPPSTRÖM M, HEDLUND K O et al. Segmentation of virus particle candidates in transmission electron microscopy images[J]. Journal of Microscopy, 245, 140-147(2012).

[3] LIU T, JONES C, SEYEDHOSSEINI M et al. A modular hierarchical approach to 3D electron microscopy image segmentation[J]. Journal of Neuroscience Methods, 226, 88-102(2014).

[4] LIU T, JURRUS E, SEYEDHOSSEINI M et al. Watershed merge tree classification for electron microscopy image segmentation[J]. Proceedings of the IAPR International Conference on Pattern Recognition International Conference on Pattern Recognition, 2012, 133-137(2012).

[5] KARABAĞ C, JONES M L, PEDDIE C J et al. Segmentation and modelling of the nuclear envelope of HeLa cells imaged with serial block face scanning electron microscopy[J]. Journal of Imaging, 5, 75(2019).

[6] FAKHRY A, ZENG T, JI S W. Residual deconvolutional networks for brain electron microscopy image segmentation[J]. IEEE Transactions on Medical Imaging, 36, 447-456(2017).

[7] XIAO C, LIU J, CHEN X et al. Deep contextual residual network for electron microscopy image segmentation in connectomics[C], 378-381(2018).

[8] JIANG Y, XIAO C, LI L L et al. An effective encoder-decoder network for neural cell bodies and cell nucleus segmentation of em images[C], 6302-6305(2019).

[9] CASSER V, KANG K, PFISTER H et al. Fast mitochondria detection for connectomics[C], 111-120(2020).

[10] CAO Y, LIU S G, PENG Y L et al. DenseUNet： densely connected UNet for electron microscopy image segmentation[J]. IET Image Processing, 14, 2682-2689(2020).

[11] QUAN T M, HILDEBRAND D G C, JEONG W K. FusionNet： a deep fully residual convolutional neural network for image segmentation in connectomics[J]. ArXiv e-Prints(2016).

[12] LUO Z R, WANG Y, LIU S K et al. Hierarchical encoder-decoder with soft label-decomposition for mitochondria segmentation in EM images[J]. Frontiers in Neuroscience, 15, 687832(2021).

[13] KHADANGI A, BOUDIER T, RAJAGOPAL V. EM-stellar： benchmarking deep learning for electron microscopy image segmentation[J]. Bioinformatics, 37, 97-106(2021).

[14] SPIERS H, SONGHURST H, NIGHTINGALE L et al. Deep learning for automatic segmentation of the nuclear envelope in electron microscopy data， trained with volunteer segmentations[J]. Traffic, 22, 240-253(2021).

[15] YUAN Z M, MA X F, YI J J et al. HIVE-Net： Centerline-aware hierarchical view-ensemble convolutional network for mitochondria segmentation in EM images[J]. Computer Methods and Programs in Biomedicine, 200, 105925(2021).

[16] OQUAB M, DARCET T, MOUTAKANNI T et al. Dinov2： Learning Robust Visual Features without Supervision[webpage](2023).

[17] KIRILLOV A, MINTUN E, RAVI N et al. Segment anything[C], 3992-4003(2023).

[18] MA J, HE Y T, LI F F et al. Segment anything in medical images[J]. Nature Communications, 15, 654(2024).

[19] WU J, FU R, FANG H et al. Medical Sam Adapter： adapting segment anything model for medical image segmentation[webpage](2023).

[20] WEI D L, LIN Z D, FRANCO-BARRANCO D et al. MitoEM dataset： Large-scale 3D Mitochondria Instance Segmentation from EM images[C], 66-76(2020).

[21] LIN Z, WEI D, PETKOVA M D et al. Nucmm Dataset： 3D neuronal nuclei instance segmentation at sub-cubic millimeter scale； proceedings of the medical image computing and computer assisted intervention-MICCAI[C](2021).

[22] HE K M, CHEN X L, XIE S N et al. Masked autoencoders are scalable vision learners[C], 15979-15988(2022).

[23] NUNEZ-IGLESIAS J, KENNEDY R, PARAG T et al. Machine learning of hierarchical clustering to segment 2D and 3D images[J]. PLoS One, 8(2013).