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SUNet++: A deep Network with Channel Attention for Small-Scale Object Segmentation on 3d medical images

清华大学学报:自然科学版(英文版) 2023年第4期28卷 628-638页

作者： Lan Zhang Kejia Zhang Haiwei Pan College of Computer Science and Technology Harbin Engineering UniversityHarbin 150001China

As a deep learning network with an encoder-decoder architecture,UNet and its series of improved versions have been widely used in medical image segmentation with great applications.However,when used to segment targets in 3d medical images such as magnetic resonance imaging(MRI),computed tomography(CT),these models do not model the relevance of images in vertical space,resulting in poor accurate analysis of consecutive slices of the same patient.On the other hand,the large amount of detail lost during the encoding process makes these models incapable of segmenting small-scale tumor targets.Aiming at the scene of small-scale target segmentation in 3d medical images,a fully new neural network model SUNet++is proposed on the basis of UNet and UNet++.SUNet++improves the existing models mainly in three aspects:1)the modeling strategy of slice superposition is used to thoroughly excavate the three dimensional information of the data;2)by adding an attention mechanism during the decoding process,small scale targets in the picture are retained and amplified;3)in the up-sampling process,the transposed convolution operation is used to further enhance the effect of the model.In order to verify the effect of the model,we collected and produced a dataset of hyperintensity MRI liver-stage images containing over 400 cases of liver nodules.Experimental results on both public and proprietary datasets demonstrate the superiority of SUNet++in small-scale target segmentation of three-dimensional medical images.

关键词： 3d medical images small-scale target segmentation attention mechanism

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Short‐term and long‐term memory self‐attention network for segmentation of tumours in 3d medical images

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CAAI Transactions on Intelligence Technology 2023年第4期8卷 1524-1537页

作者： Mingwei Wen Quan Zhou Bo Tao Pavel Shcherbakov Yang Xu Xuming Zhang department of Biomedical Engineering College of Life Science and TechnologyHuazhong University of Science and TechnologyWuhanChina State Key Laboratory of digital Manufacturing Equipment and Technology Huazhong University of Science and TechnologyWuhanChina Institute for Control Science Russian Academy of SciencesMoscowRussia Hubei medical devices Quality Supervision and Test Institute WuhanChina

Tumour segmentation in medical images(especially 3d tumour segmentation)is highly challenging due to the possible similarity between tumours and adjacent tissues,occurrence of multiple tumours and variable tumour shapes and sizes.The popular deep learning‐based segmentation algorithms generally rely on the convolutional neural network(CNN)and Transformer.The former cannot extract the global image features effectively while the latter lacks the inductive bias and involves the complicated computation for 3d volume data.The existing hybrid CNN‐Transformer network can only provide the limited performance improvement or even poorer segmentation performance than the pure CNN.To address these issues,a short‐term and long‐term memory self‐attention network is proposed.Firstly,a distinctive self‐attention block uses the Transformer to explore the correlation among the region features at different levels extracted by the CNN.Then,the memory structure filters and combines the above information to exclude the similar regions and detect the multiple tumours.Finally,the multi‐layer reconstruction blocks will predict the tumour boundaries.Experimental results demonstrate that our method outperforms other methods in terms of subjective visual and quantitative evaluation.Compared with the most competitive method,the proposed method provides dice(82.4%vs.76.6%)and Hausdorff distance 95%(Hd95)(10.66 vs.11.54 mm)on the KiTS19 as well as dice(80.2%vs.78.4%)and Hd95(9.632 vs.12.17 mm)on the LiTS.

关键词： 3d medical images convolutional neural network self‐attention network transformer tumor segmentation

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