Bray F, Laversanne M, Weiderpass E, Soerjomataram I. The ever-increasing importance of cancer as a leading cause of premature death worldwide. Cancer;127(16):3029–3030. 2021.
Milletari F, Navab N, Ahmadi SA. V-net: Fully convolutional neural networks for volumetric medical image segmentation. In: 2016 fourth international conference on 3D vision (3DV). Ieee. p. 565–571. ; 2016.
Pereira S, Pinto A, Alves V, Silva CA. Brain tumor segmentation using convolutional neural networks in MRI images. IEEE transactions on medical imaging;35(5):1240–1251. 2016.
Bernard O, Lalande A, Zotti C, Cervenansky F, Yang X, Heng PA, et al. Deep learning techniques for automatic MRI cardiac multi-structures segmentation and diagnosis: is the problem solved? IEEE transactions on medical imaging;37(11):2514–2525. 2018.
Wang G, Li W, Zuluaga MA, Pratt R, Patel PA, Aertsen M, et al. Interactive medical image segmentation using deep learning with image-specific fine tuning. IEEE transactions on medical imaging;37(7):1562–1573. 2018.
Cao G, Sun Z, Wang C, Geng H, Fu H, Yin Z, et al. RASNet: Renal automatic segmentation using an improved U-Net with multi-scale perception and attention unit. Pattern Recognition;150:110336. 2024.
Jafari E, Zarei A, Dadgar H, Keshavarz A, Manafi-Farid R, Rostami H, et al. A convolutional neural network–based system for fully automatic segmentation of whole-body [68Ga] Ga-PSMA PET images in prostate cancer. European Journal of Nuclear Medicine and Molecular Imaging;51(5):1476–1487. 2024.
Article CAS PubMed Google Scholar
Garg G, Juneja M. A survey of prostate segmentation techniques in different imaging modalities. Current Medical Imaging Reviews;14(1):19–46. 2018.
Wang L, Fang S, Zhang C, Li R, Duan C. Efficient hybrid transformer: Learning global-local context for urban sence segmentation. arXiv e-prints;p. arXiv–2109. 2021.
Liu W. Parsenet: Looking wider to see better. arXiv preprint arXiv:1506.04579;. 2015.
Yu F. Multi-scale context aggregation by dilated convolutions. arXiv preprint arXiv:1511.07122;. 2015.
Chen LC, Papandreou G, Kokkinos I, Murphy K, Yuille AL. Deeplab: Semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected crfs. IEEE transactions on pattern analysis and machine intelligence;40(4):834–848. 2017.
Gu Z, Cheng J, Fu H, Zhou K, Hao H, Zhao Y, et al. Ce-net: Context encoder network for 2d medical image segmentation. IEEE transactions on medical imaging;38(10):2281–2292. 2019.
Li W, Wang G, Fidon L, Ourselin S, Cardoso MJ, Vercauteren T. On the compactness, efficiency, and representation of 3D convolutional networks: brain parcellation as a pretext task. In: Information Processing in Medical Imaging: 25th International Conference, IPMI 2017, Boone, NC, USA, June 25-30, 2017, Proceedings 25. Springer. p. 348–360. ; 2017.
Xie G, Ren J, Marshall S, Zhao H, Li R, Chen R. Self-attention enhanced deep residual network for spatial image steganalysis. Digital signal processing;139:104063. 2023.
Vaswani A. Attention is all you need. Advances in Neural Information Processing Systems;. 2017.
Carion N, Massa F, Synnaeve G, Usunier N, Kirillov A, Zagoruyko S. End-to-end object detection with transformers. In: European conference on computer vision. Springer. p. 213–229. ; 2020.
Chen M, Radford A, Child R, Wu J, Jun H, Luan D, et al. Generative pretraining from pixels. In: International conference on machine learning. PMLR. p. 1691–1703. ; 2020.
Liu Z, Lin Y, Cao Y, Hu H, Wei Y, Zhang Z, et al. Swin transformer: Hierarchical vision transformer using shifted windows. In: Proceedings of the IEEE/CVF international conference on computer vision. p. 10012–10022. ; 2021.
Zheng S, Lu J, Zhao H, Zhu X, Luo Z, Wang Y, et al. Rethinking semantic segmentation from a sequence-to-sequence perspective with transformers. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. p. 6881–6890. ; 2021.
Battaglia PW, Hamrick JB, Bapst V, Sanchez-Gonzalez A, Zambaldi V, Malinowski M, et al. Relational inductive biases, deep learning, and graph networks. arXiv preprint arXiv:1806.01261;. 2018.
Dosovitskiy A. An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:2010.11929;. 2020.
Neyshabur B. Towards learning convolutions from scratch. Advances in Neural Information Processing Systems;33:8078–8088. 2020.
Xu Y, Zhang Q, Zhang J, Tao D. Vitae: Vision transformer advanced by exploring intrinsic inductive bias. Advances in neural information processing systems;34:28522–28535. 2021.
Ronneberger O, Fischer P, Brox T. U-net: Convolutional networks for biomedical image segmentation. In: Medical image computing and computer-assisted intervention–MICCAI 2015: 18th international conference, Munich, Germany, October 5-9, 2015, proceedings, part III 18. Springer. p. 234–241. ; 2015.
Ghose S, Oliver A, Marti R, Lladó X, Freixenet J, Vilanova JC, et al. Prostate segmentation with local binary patterns guided active appearance models. In: Medical Imaging 2011: Image Processing. vol. 7962. SPIE. p. 389–396. ; 2011.
Li X, Li C, Fedorov A, Kapur T, Yang X. Segmentation of prostate from ultrasound images using level sets on active band and intensity variation across edges. Medical physics;43(6Part1):3090–3103. 2016.
Yu Y, Chen Y, Chiu B. Fully automatic prostate segmentation from transrectal ultrasound images based on radial bas-relief initialization and slice-based propagation. Computers in biology and medicine;74:74–90. 2016.
Geng L, Li S, Xiao Z, Zhang F. Multi-channel feature pyramid networks for prostate segmentation, based on transrectal ultrasound imaging. Applied Sciences;10(11):3834. 2020.
Wang W, Pan B, Ai Y, Li G, Fu Y, Liu Y. ParaCM-PNet: A CNN-tokenized MLP combined parallel dual pyramid network for prostate and prostate cancer segmentation in MRI. Computers in Biology and Medicine;170:107999. 2024.
Jiang H, Imran M, Muralidharan P, Patel A, Pensa J, Liang M, et al. MicroSegNet: A deep learning approach for prostate segmentation on micro-ultrasound images. Computerized Medical Imaging and Graphics;112:102326. 2024.
Belal SL, Frantz S, Minarik D, Enqvist O, Wikström E, Edenbrandt L, et al. Applications of artificial intelligence in PSMA PET/CT for prostate cancer imaging. In: Seminars in nuclear medicine. vol. 54. Elsevier. p. 141–149. ; 2024.
Zaev R, Romanov AY, Solovyev R. Segmentation of prostate cancer on TRUS images using ML. In: 2023 International Russian Smart Industry Conference (SmartIndustryCon). IEEE. p. 460–465. ; 2023.
Khalkhali V, Azim SM, Dehzangi I. ExShall-CNN: An Explainable Shallow Convolutional Neural Network for Medical Image Segmentation. Machine Learning and Knowledge Extraction;7(1):19. 2025.
Zhang L, Guo X, Sun H, Wang W, Yao L. Alternate encoder and dual decoder CNN-Transformer networks for medical image segmentation. Scientific Reports;15(1):8883. 2025.
Tolstikhin IO, Houlsby N, Kolesnikov A, Beyer L, Zhai X, Unterthiner T, et al. Mlp-mixer: An all-mlp architecture for vision. Advances in neural information processing systems;34:24261–24272. 2021.
Touvron H, Bojanowski P, Caron M, Cord M, El-Nouby A, Grave E, et al. Resmlp: Feedforward networks for image classification with data-efficient training. IEEE transactions on pattern analysis and machine intelligence;45(4):5314–5321. 2022.
Ding X, Xia C, Zhang X, Chu X, Han J, Ding G. Repmlp: Re-parameterizing convolutions into fully-connected layers for image recognition. arXiv preprint arXiv:2105.01883;. 2021.
Chen J, Lu Y, Yu Q, Luo X, Adeli E, Wang Y, et al. Transunet: Transformers make strong encoders for medical image segmentation. arXiv preprint arXiv:2102.04306;. 2021.
Cao H, Wang Y, Chen J, Jiang D, Zhang X, Tian Q, et al. Swin-unet: Unet-like pure transformer for medical image segmentation. In: European conference on computer vision. Springer. p. 205–218. ; 2022.
Gu A, Dao T. Mamba: Linear-time sequence modeling with selective state spaces. arXiv preprint arXiv:2312.00752;. 2023.
Kirillov A, Mintun E, Ravi N, Mao H, Rolland C, Gustafson L, et al. Segment anything. In: Proceedings of the IEEE/CVF international conference on computer vision. p. 4015–4026. ; 2023.
Zhang H, Lian J, Yi Z, Wu R, Lu X, Ma P, et al. HAU-Net: Hybrid CNN-transformer for breast ultrasound image segmentation. Biomedical Signal Processing and Control;87:105427. 2024.
Gowda SN, Clifton DA. Cc-sam: Sam with cross-feature attention and context for ultrasound image segmentation. In: European Conference on Computer Vision. Springer. p. 108–124. ; 2024.
Zhang M, Chen Z, Ge Y, Tao X. HMT-UNet: a hybird mamba-transformer vision UNet for medical image segmentation. arXiv preprint arXiv:2408.11289;. 2024.
Fan Y, Song J, Yuan L, Jia Y. HCT-Unet: multi-target medical image segmentation via a hybrid CNN-transformer Unet incorporating multi-axis gated multi-layer perceptron. The Visual Computer;41(5):3457–3472. 2025.
Lei Ba J, Kiros JR, Hinton GE. Layer normalization. ArXiv e-prints;p. arXiv–1607. 2016.
He K, Zhang X, Ren S, Sun J. Deep residual learning for image recognition. In: Proceedings of the IEEE conference on computer vision and pattern recognition. p. 770–778. ; 2016.
Zhou Z, Siddiquee MMR, Tajbakhsh N, Liang J. Unet++: Redesigning skip connections to exploit multiscale features in image segmentation. IEEE transactions on medical imaging;39(6):1856–1867. 2019.
Oktay O, Schlemper J, Folgoc LL, Lee M, Heinrich M, Misawa K, et al. Attention u-net: Learning where to look for the pancreas. arXiv preprint arXiv:1804.03999;. 2018.
Huang H, Xie S, Lin L, Iwamoto Y, Han X, Chen YW, et al. ScaleFormer: revisiting the transformer-based backbones from a scale-wise perspective for medical image segmentation. arXiv preprint arXiv:2207.14552;. 2022.
Valanarasu JMJ, Patel VM. Unext: Mlp-based rapid medical image segmentation network. In: International conference on medical image computing and computer-assisted intervention. Springer. p. 23–33. ; 2022.
Tang X, Li J, Liu Q, Zhou C, Zeng P, Meng Y, et al. SWMA-UNet: multi-path attention network for improved medical image segmentation. IEEE Journal of Biomedical and Health Informatics;. 2024.
Wu J, Wang Z, Hong M, Ji W, Fu H, Xu Y, et al. Medical sam adapter: Adapting segment anything model for medical image segmentation. Medical image analysis;102:103547. 2025.
Isensee F, Jaeger PF, Kohl SA, Petersen J, Maier-Hein KH. nnU-Net: a self-configuring method for deep learning-based biomedical image segmentation. Nature methods;18(2):203–211. 2021.
Sajua GA, Akhib M, Chang Y. AgentMRI: A Vison Language Model-Powered AI System for Self-regulating MRI Reconstruction with Multiple Degradations. Journal of Imaging Informatics in Medicine;p. 1–19. 2025.
Comments (0)