SAU-Net: Multiorgan Image Segmentation Method Improved Using Squeeze Attention Mechanism

Guogang Cao; Hongdong Mao; Shu Zhang; Ying Chen; Cuixia Dai

doi:10.3788/LOP202259.0417003

[1] Long Y, Mi K, Fan J C et al. Prognosis and related influencing factors of HPV-positive head and neck squamous cell carcinoma[J]. Journal of Cancer Control and Treatment, 24, 196-200(2011).

[2] Wang Z S, Wei L F, Wang L et al. Hierarchical vertex regression-based segmentation of head and neck CT images for radiotherapy planning[J]. IEEE Transactions on Image Processing, 27, 923-937(2018).

[3] Lin G, Adiga U, Olson K et al. A hybrid 3D watershed algorithm incorporating gradient cues and object models for automatic segmentation of nuclei in confocal image stacks[J]. Cytometry Part A, 56, 23-36(2003).

[4] Lu J F, Lin H, Pan Z G. Adaptive region growing algorithm in medical images segmentation[J]. Journal of Computer Aided Design & Computer Graphics, 17, 2168-2173(2005).

[5] Lei X L, Yu X S, Chi J N et al. Brain tumor segmentation in MR images using a sparse constrained level set algorithm[J]. Expert Systems With Applications, 168, 114262(2021).

[6] Long J, Shelhamer E, Darrell T. Fully convolutional networks for semantic segmentation[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 39, 640-651(2015).

[7] Ronneberger O, Fischer P, Brox T. U-net: convolutional networks for biomedical image segmentation[M]. Navab N, Hornegger J, Wells W M, et al. Medical image computing and computer-assisted intervention-MICCAI 2015, 9351, 234-241(2015).

[8] Çiçek Ö, Abdulkadir A, Lienkamp S S et al. 3D U-net: learning dense volumetric segmentation from sparse annotation[M]. Ourselin S, Joskowicz L, Sabuncu M R, et al. Medical image computing and computer-assisted intervention-MICCAI 2016, 9901, 424-432(2016).

[9] Yi S L, Wang T W, Yang X L et al. Improved lung field segmentation method of u-net network[J]. Laser & Optoelectronics Progress, 59, 0210010(2022).

[10] Feng B W, Lü X Q, Gu Y et al. Three-dimensional parallel convolution neural network brain tumor segmentation based on dilated convolution[J]. Laser & Optoelectronics Progress, 57, 141009(2020).

[11] Zhang W X, Zhu Z C, Zhang Y H et al. Cell image segmentation method based on residual block and attention mechanism[J]. Acta Optica Sinica, 40, 1710001(2020).

[12] Li D X, Zhang Z. Improved U-net segmentation algorithm for the retinal blood vessel images[J]. Acta Optica Sinica, 40, 1010001(2020).

[13] Raudaschl P F, Zaffino P, Sharp G C et al. Evaluation of segmentation methods on head and neck CT: auto-segmentation challenge 2015[J]. Medical Physics, 44, 2020-2036(2017).

[14] Ren X, Xiang L, Nie D et al. Interleaved 3D-CNNs for joint segmentation of small-volume structures in head and neck CT images[J]. Medical Physics, 45, 2063-2075(2018).

[15] Hu J, Shen L, Albanie S et al. Squeeze-and-excitation networks[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 42, 2011-2023(2020).

[16] Lin T Y, Goyal P, Girshick R et al. Focal loss for dense object detection[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 42, 318-327(2020).

[17] Zhu W, Huang Y, Zeng L et al. AnatomyNet: deep learning for fast and fully automated whole-volume segmentation of head and neck anatomy[J]. Medical Physics, 46, 576-589(2019).

[18] Tappeiner E, Pröll S, Hönig M et al. Multi-organ segmentation of the head and neck area: an efficient hierarchical neural networks approach[J]. International Journal of Computer Assisted Radiology and Surgery, 14, 745-754(2019).

[19] Gao Y H, Huang R, Chen M et al. FocusNet: imbalanced large and small organ segmentation with an end-to-end deep neural network for head and neck CT images[EB/OL]. https://arxiv.org/abs/1907.12056

[20] Tappeiner E, Pröll S, Fritscher K et al. Training of head and neck segmentation networks with shape prior on small datasets[J]. International Journal of Computer Assisted Radiology and Surgery, 15, 1417-1425(2020).

[21] Gao Y H, Huang R, Yang Y W et al. FocusNetv2: imbalanced large and small organ segmentation with adversarial shape constraint for head and neck CT images[J]. Medical Image Analysis, 67, 101831(2021).

[22] Dai X J, Lei Y, Wang T H et al. Head-and-neck organs-at-risk auto-delineation using dual pyramid networks for CBCT-guided adaptive radiotherapy[J]. Physics in Medicine & Biology, 66, 045021(2021).

[23] Zhong Z L, Lin Z Q, Bidart R et al. Squeeze-and-attention networks for semantic segmentation[C], 13062-13071(2020).

[24] Salehi S S M, Erdogmus D, Gholipour A. Tversky loss function for image segmentation using 3D fully convolutional deep networks[M]. Wang Q, Shi Y H, Suk H I. Machine learning in medical imaging, 10541, 379-387(2017).

[25] Clark K, Vendt B, Smith K et al. The cancer imaging archive (TCIA): maintaining and operating a public information repository[J]. Journal of Digital Imaging, 26, 1045-1057(2013).

[26] Vallières M, Kay-Rivest E, Perrin L J et al. Radiomics strategies for risk assessment of tumor failure in head-and-neck cancer[J]. Scientific Reports, 7, 10117(2017).