A Systematic Literature Review on Deep Learning Based Medical Image Segmentation
Keywords:
Medical imaging, deep learning, SegmentationAbstract
Medical imaging becoming an essential life supporting aspect in the current world. It having various types of modalities and each one serves for specific applications. Lot of applications and necessities are there to figure out the various life-threatening diseases. But the identification of abnormalities is not so easy doing manually. It is error prone and time consuming. And also requires lot of proficiency and experience. Deep learning is a state of art methodology which having a huge span of applications especially in medical field. Particularly, in medical imaging the deep learning methods can be applied and can make huge differences in the accuracy of findings. They can be used for synthesis, segmentation, and classification. This study is aimed to focus on the different types of medical imaging modalities and the various deep learning algorithms on medical imaging. The performances of different methods were compared by means of various evaluation metrics.
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Minaee, S., Boykov, Y. Y., Porikli, F., Plaza, A. J., Kehtarnavaz, N., & Terzopoulos, D. (2021). Image segmentation using Deep Learning: A Survey. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1–1. https://doi.org/10.1109/tpami.2021.3059968
Li, Y., Wu, B., & Ge, X. (2019). Structural segmentation and classification of Mobile Laser Scanning Point Clouds with large variations in point density. ISPRS Journal of Photogrammetry and Remote Sensing, 153, 151–165. https://doi.org/10.1016/j.isprsjprs.2019.05.007
Meyer-Bäse Anke, & Schmid, V. (2014). Pattern recognition and signal analysis in medical imaging. Elsevier/Academic Press.
Qiu, W., Chen, Y., Kishimoto, J., de Ribaupierre, S., Chiu, B., Fenster, A., & Yuan, J. (2017). Automatic segmentation approach to extracting neonatal cerebral ventricles from 3D ultrasound images. Medical Image Analysis, 35, 181–191. https://doi.org/10.1016/j.media.2016.06.038
Qiu, W., Yuan, J., Kishimoto, J., Ukwatta, E., & Fenster, A. (2013). Lateral ventricle segmentation of 3D pre-term neonates US using convex optimization. Medical Image Computing and Computer-Assisted Intervention – MICCAI 2013, 559–566. https://doi.org/10.1007/978-3-642-40760-4_70
Qiu, W., Yuan, J., Kishimoto, J., McLeod, J., Chen, Y., de Ribaupierre, S., & Fenster, A. (2015). User-guided segmentation of preterm neonate ventricular system from 3-D ultrasound images using convex optimization. Ultrasound in Medicine & Biology, 41(2), 542–556. https://doi.org/10.1016/j.ultrasmedbio.2014.09.019
Boucher, M.-A., Lippé, S., Damphousse, A., El-Jalbout, R., & Kadoury, S. (2018). Dilatation of lateral ventricles with brain volumes in infants with 3D Transfontanelle US. Medical Image Computing and Computer Assisted Intervention – MICCAI 2018, 557–565. https://doi.org/10.1007/978-3-030-00931-1_64
Sciolla, B., Martin, M., Delachartre, P., & Quetin, P. (2016). Segmentation of the lateral ventricles in 3D ultrasound images of the brain in neonates. 2016 IEEE International Ultrasonics Symposium (IUS). https://doi.org/10.1109/ultsym.2016.7728560
Nie, D., Wang, L., Adeli, E., Lao, C., Lin, W., & Shen, D. (2019). 3-D fully convolutional networks for multimodal isointense infant brain image segmentation. IEEE Transactions on Cybernetics, 49(3), 1123–1136. https://doi.org/10.1109/tcyb.2018.2797905
Singh, V., Sridar, P., Kim, J., Nanan, R., Poornima, N., Priya, S., Reddy, G. S., Chandrasekaran, S., & Krishnakumar, R. (2021). Semantic segmentation of cerebellum in 2D fetal ultrasound brain images using convolutional neural networks. IEEE Access, 9, 85864–85873. https://doi.org/10.1109/access.2021.3088946
Zhang, J., Jiang, Z., Dong, J., Hou, Y., & Liu, B. (2020). Attention gate RESU-net for automatic MRI brain tumor segmentation. IEEE Access, 8, 58533–58545. https://doi.org/10.1109/access.2020.2983075
Long, J., Shelhamer, E., & Darrell, T. (2015). Fully convolutional networks for semantic segmentation. 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). https://doi.org/10.1109/cvpr.2015.7298965
Fuerst, B., Wein, W., Müller, M., & Navab, N. (2014). Automatic ultrasound–MRI registration for neurosurgery using the 2D and 3D LC2 metric. Medical Image Analysis, 18(8), 1312–1319. https://doi.org/10.1016/j.media.2014.04.008
Wang, P., Cuccolo, N. G., Tyagi, R., Hacihaliloglu, I., & Patel, V. M. (2018). Automatic real-time CNN-based neonatal brain ventricles segmentation. 2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018). https://doi.org/10.1109/isbi.2018.8363674
Martin, M., Sciolla, B., Sdika, M., Wang, X., Quetin, P., & Delachartre, P. (2018). Automatic segmentation of the cerebral ventricle in neonates using deep learning with 3D reconstructed freehand ultrasound imaging. 2018 IEEE International Ultrasonics Symposium (IUS). https://doi.org/10.1109/ultsym.2018.8580214
Yaqub, M., Cuingnet, R., Napolitano, R., Roundhill, D., Papageorghiou, A., Ardon, R., & Noble, J. A. (2013). Volumetric segmentation of key fetal brain structures in 3D ultrasound. Machine Learning in Medical Imaging, 25–32. https://doi.org/10.1007/978-3-319-02267-3_4
Yaqub, M., Javaid, M. K., Cooper, C., & Noble, J. A. (2014). Investigation of the role of feature selection and weighted voting in random forests for 3-D volumetric segmentation. IEEE Transactions on Medical Imaging, 33(2), 258–271. https://doi.org/10.1109/tmi.2013.2284025
Reyes López, M., Arámbula Cosío, F., Escalante Ramírez, B., & Olveres Montiel, J. (2018). Shape model and hermite features for the segmentation of the cerebellum in fetal ultrasound. 14th International Symposium on Medical Information Processing and Analysis. https://doi.org/10.1117/12.2511411
Vargas-Quintero, L., Escalante-Ramírez, B., Camargo Marín, L., Guzmán Huerta, M., Arámbula Cosio, F., & Borboa Olivares, H. (2016). Left ventricle segmentation in fetal echocardiography using a multi-texture active appearance model based on the steered hermite transform. Computer Methods and Programs in Biomedicine, 137, 231–245. https://doi.org/10.1016/j.cmpb.2016.09.021
Jia, X., Liu, Y., Yang, Z., & Yang, D. (2020). Multi-modality self-attention aware deep network for 3D biomedical segmentation. BMC Medical Informatics and Decision Making, 20(S3). https://doi.org/10.1186/s12911-020-1109-0
Khan, A., Kim, H., & Chua, L. (2021). PMED-net: Pyramid based multi-scale encoder-decoder network for medical image segmentation. IEEE Access, 9, 55988–55998. https://doi.org/10.1109/access.2021.3071754
Wang, R., Ma, Y., Sun, W., Guo, Y., Wang, W., Qi, Y., & Gong, X. (2019). Multi-level nested Pyramid Network for mass segmentation in mammograms. Neurocomputing, 363, 313–320. https://doi.org/10.1016/j.neucom.2019.06.045
Roth, H. R., Shen, C., Oda, H., Sugino, T., Oda, M., Hayashi, Y., Misawa, K., & Mori, K. (2018). A multi-scale pyramid of 3d fully convolutional networks for abdominal multi-organ segmentation. Medical Image Computing and Computer Assisted Intervention – MICCAI 2018, 417–425. https://doi.org/10.1007/978-3-030-00937-3_48
Jose Valanarasu, J. M., Yasarla, R., Wang, P., Hacihaliloglu, I., & Patel, V. M. (2020). Learning to segment brain anatomy from 2D ultrasound with less data. IEEE Journal of Selected Topics in Signal Processing, 14(6), 1221–1234. https://doi.org/10.1109/jstsp.2020.3001513
Reddy, K. K., Solmaz, B., Yan, P., Avgeropoulos, N. G., Rippe, D. J., & Shah, M. (2012). Confidence guided enhancing brain tumor segmentation in multi-parametric MRI. 2012 9th IEEE International Symposium on Biomedical Imaging (ISBI). https://doi.org/10.1109/isbi.2012.6235560
Gu, R., Wang, G., Song, T., Huang, R., Aertsen, M., Deprest, J., Ourselin, S., Vercauteren, T., & Zhang, S. (2021). CA-net: Comprehensive attention convolutional neural networks for explainable medical image segmentation. IEEE Transactions on Medical Imaging, 40(2), 699–711. https://doi.org/10.1109/tmi.2020.3035253
Cai, Z., & Vasconcelos, N. (2021). Cascade R-CNN: High Quality Object Detection and instance segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 43(5), 1483–1498. https://doi.org/10.1109/tpami.2019.2956516
Visin, F., Romero, A., Cho, K., Matteucci, M., Ciccone, M., Kastner, K., Bengio, Y., & Courville, A. (2016). ReSeg: A recurrent neural network-based model for semantic segmentation. 2016 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW). https://doi.org/10.1109/cvprw.2016.60
Xiang, Y., Chung, A. C. S., & Ye, J. (2006). An active contour model for image segmentation based on Elastic Interaction. Journal of Computational Physics, 219(1), 455–476. https://doi.org/10.1016/j.jcp.2006.03.026
Teng, L., Li, H., & Karim, S. (2019). DMCNN: A deep multiscale convolutional neural network model for medical image segmentation. Journal of Healthcare Engineering, 2019, 1–10. https://doi.org/10.1155/2019/8597606
Jha, D., Riegler, M. A., Johansen, D., Halvorsen, P., & Johansen, H. D. (2020). DoubleU-net: A deep convolutional neural network for medical image segmentation. 2020 IEEE 33rd International Symposium on Computer-Based Medical Systems (CBMS). https://doi.org/10.1109/cbms49503.2020.00111
Beneš, Miroslav., & Zitová, Barbara. (2014). Performance evaluation of image segmentation algorithms on microscopic image data. Journal of Microscopy, 257(1), 65–85. https://doi.org/10.1111/jmi.12186
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