Medical image segmentation is a crucial preliminary step for a number of downstream diagnosis tasks.
As deep convolutional neural networks successfully promote the development of computer vision, it is possible to
make medical image segmentation a semi-automatic procedure by applying deep convolutional neural networks
to finding the contours of regions of interest that are then revised by radiologists. However, supervised learning
necessitates large annotated data, which are difficult to acquire especially for medical images. Self-supervised
learning is able to take advantage of unlabeled data and provide good initialization to be finetuned for downstream
tasks with limited annotations. Considering that most self-supervised learning especially contrastive learning
methods are tailored to natural image classification and entail expensive GPU resources, we propose a novel and
simple pretext-based self-supervised learning method that exploits the value of positional information in volumetric
medical images. Specifically, we regard spatial coordinates as pseudo labels and pretrain the model by predicting
positions of randomly sampled 2D slices in volumetric medical images. Experiments on four semantic segmentation
datasets demonstrate the superiority of our method over other self-supervised learning methods in both semisupervised learning and transfer learning settings. Codes are available at https://github.com/alienzyj/PPos.
赵寅杰1，侯润萍1，曾琬琴2，秦玉磊1，沈天乐2，徐志勇2，傅小龙2，沈红斌1
. Positional Information is a Strong Supervision for Volumetric Medical Image Segmentation[J]. Journal of Shanghai Jiaotong University(Science), 2025
, 30(1)
: 121
-129
.
DOI: 10.1007/s12204-023-2614-y
[1] TAGHANAKI S A, ABHISHEK K, COHEN J P, et al. Deep semantic segmentation of natural and medical images: A review [J]. Artificial Intelligence Review, 2021, 54(1): 137-178.
[2] ZHANG S, XU J C, CHEN Y C, et al. Revisiting 3D context modeling with supervised pre-training for universal lesion detection in CT slices [M]//Medical image computing and computer assisted intervention—MICCAI 2020. Cham: Springer, 2020: 542-551.
[3] JING L L, TIAN Y L. Self-supervised visual feature learning with deep neural networks: A survey [J].IEEE Transactions on Pattern Analysis and Machine Intelligence, 2021, 43(11): 4037-4058.
[4] CHEN T, KORNBLITH S, NOROUZI M, et al. A simple framework for contrastive learning of visual representations [C]//37th International Conference on Machine Learning. Vienna: IMLS, 2020: 1597-1607.
[5] HE K M, FAN H Q, WU Y X, et al. Momentum contrast for unsupervised visual representation learning [C]//2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Seattle: IEEE, 2020: 9726-9735.
[6] GRILL J B, STRUB F, ALTCHE F, et al. Bootstrap ′your own latent: A new approach to self-supervised learning [C]//34th Conference on Neural Information Processing Systems. Vancouver: NIPS, 2020: 21271-21284.
[7] WU Z R, XIONG Y J, YU S X, et al. Unsupervised feature learning via non-parametric instance discrimination [C]//2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Salt Lake City: IEEE, 2018: 3733-3742.
[8] CHAITANYA K, ERDIL E, KARANI N, et al. Contrastive learning of global and local features for medical image segmentation with limited annotations [C]//34th Conference on Neural Information Processing Systems. Vancouver: NIPS, 2020: 12546-12558.
[9] ZENG D W, WU Y W, HU X R, et al. Positional contrastive learning for volumetric medical image segmentation [M]//Medical image computing and computer assisted intervention— MICCAI 2021. Cham:Springer, 2021: 221-230.
[10] RONNEBERGER O, FISCHER P, BROX T. UNet: Convolutional networks for biomedical image segmentation [M]//Medical image computing and computer-assisted intervention— MICCAI 2015. Cham: Springer, 2015: 234-241.
[11] MILLETARI F, NAVAB N, AHMADI S A. V-Net: Fully convolutional neural networks for volumetric medical image segmentation [C]//2016 Fourth International Conference on 3D Vision. Stanford: IEEE,2016: 565-571.
[12] C? IC?EK O, ABDULKADIR A, LIENKAMP S S, et al. 3D U-net: Learning dense volumetric segmentation from sparse annotation [M]//Medical image computing and computer-assisted intervention— MICCAI 2016. Cham: Springer, 2016: 424-432.
[13] LOU A, GUAN S, LOEW M. DC-UNet: Rethinking the U-Net architecture with dual channel efficient CNN for medical image segmentation [C]//Medical Imaging 2021: Image Processing. Online: SPIE, 2021, 11596: 758-768.
[14] ZHOU Z W, SIDDIQUEE M M R, TAJBAKHSH N, et al. UNet: Redesigning skip connections to exploit multiscale features in image segmentation [J]. IEEE Transactions on Medical Imaging, 2020, 39(6): 1856-1867.
[15] ISENSEE F, JAEGER P F, KOHL S A A, et al. nnUNet: A self-configuring method for deep learning-based biomedical image segmentation [J]. Nature Methods, 2021, 18(2): 203-211.
[16] NOROOZI M, FAVARO P. Unsupervised learning of visual representations by solving jigsaw puzzles [M]//Computer vision — ECCV 2016. Cham: Springer, 2016: 69-84.
[17] DOERSCH C, GUPTA A, EFROS A A. Unsupervised visual representation learning by context prediction [C]//2015 IEEE International Conference on Computer Vision. Santiago: IEEE, 2015: 1422-1430.
[18] ZHANG R, ISOLA P, EFROS A A. Colorful image colorization [M]//Computer vision — ECCV 2016. Cham: Springer International Publishing, 2016: 649-666.
[19] PATHAK D, KRAHENB ¨ UHL P, DONAHUE J, et ¨ al. Context encoders: Feature learning by inpainting [C]//2016 IEEE Conference on Computer Vision and Pattern Recognition. Las Vegas: IEEE, 2016: 2536-2544.
[20] KHOSLA P, TETERWAK P, WANG C, et al. Supervised contrastive learning [C]// 34th Conference on Neural Information Processing Systems. Vancouver: NIPS, 2020: 18661-18673.
[21] CHEN X L, HE K M. Exploring simple Siamese representation learning [C]//2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Nashville: IEEE, 2021: 15745-15753.
[22] ZHOU Z W, SODHA V, RAHMAN SIDDIQUEE M M, et al. Models genesis: generic autodidactic models for 3D medical image analysis [M]//Medical image computing and computer assisted intervention— MICCAI 2019. Cham: Springer, 2019: 384-393.
[23] ZHOU Z W, SODHA V, PANG J X, et al. Models genesis [J]. Medical Image Analysis, 2021, 67: 101840.
[24] ZHUANG X R, LI Y X, HU Y F, et al. Self-supervised feature learning for 3D medical images by playing a rubik’s cube [M]//Medical image computing and computer assisted intervention— MICCAI 2019. Cham: Springer, 2019: 420-428.
[25] ZHU J W, LI Y X, HU Y F, et al. Rubik’s Cube+: A self-supervised feature learning framework for 3D medical image analysis [J]. Medical Image Analysis, 2020, 64: 101746.
[26] HAGHIGHI F, TAHER M R H, ZHOU Z W, et al. Transferable visual words: Exploiting the semantics of anatomical patterns for self-supervised learning [J]. IEEE Transactions on Medical Imaging, 2021, 40(10): 2857-2868.
[27] YAN K, LU L, SUMMERS R M. Unsupervised body part regression via spatially self-ordering convolutional neural networks [C]//2018 IEEE 15th International Symposium on Biomedical Imaging. Washington: IEEE, 2018: 1022-1025.
