GAPNet: Granularity Attention Network with Anatomy-Prior-Constraint for Carotid Artery Segmentation
Article Sidebar
Published
Sep 21, 2024
Main Article Content
Abstract
Atherosclerosis is a chronic, progressive disease that primarily affects the arterial walls. It is one of the major causes of cardiovascular disease. Magnetic Resonance (MR) black-blood vessel wall imaging (BB-VWI) offers crucial insights into vascular disease diagnosis by clearly visualizing vascular structures. However, the complex anatomy of the neck poses challenges in distinguishing the carotid artery (CA) from surrounding structures, especially with changes like atherosclerosis. In order to address these issues, we propose GAPNet, which is a consisting of a novel geometric prior deduced from an anatomical viewpoint. The use of anatomical prior allows the model to avoid segmentation contours whose topology violates the anatomical reality. Specifically, at the first stage, regional features are learned to identify the location of the target CA and to reduce the influence from the surrounding similar tissues. The second stage aims to improve the feature representation capability, by employing a delicately designed Feature Refinement Attention (FRA) module to capture boundary and detailed information alongside a new Multi-Scale Information Enhancement (MIE) module at the end of the decoder procedure. Experimental results demonstrate the superior performance of our approach on two carotid artery datasets, respectively achieving Dice scores of 0.76 and 0.83, proving the effectiveness of GAPNet in improving the accuracy of carotid artery segmentation in MR imaging.
Article Details
How to Cite
Zhang, L., Lu, C., Shi, X., Shan, C., Zhang, J., Chen, D., & Cohen, L.
(2024).
GAPNet: Granularity Attention Network with Anatomy-Prior-Constraint for Carotid Artery Segmentation.
Proceedings Of The Conference Algoritmy, , 99 - 108.
Retrieved from http://www.iam.fmph.uniba.sk/amuc/ojs/index.php/algoritmy/article/view/2159/1031
Section
Articles
References
[1] Tsao, C.W., Aday, A.W., Almarzooq, Z.I., Alonso, A., Beaton, A.Z., Bittencourt, M.S., Boehme, A.K., Buxton, A.E., Carson, A.P., Commodore-Mensah, Y., et al.: Heart disease and stroke statistics—2022 update: a report from the american heart association. Circulation 145(8) (2022) e153–e639
[2] Chen, H., Zhao, X., Dou, J., Du, C., Yang, R., Sun, H., Yu, S., Zhao, H., Yuan, C., Balu, N.: Carotid vessel wall segmentation and atherosclerosis diagnosis challenge(2022). https: //vessel-wall-segmentation-2022.grand-challenge.org/
[3] Seabra, J.C., Pedro, L.M., e Fernandes, J.F., Sanches, J.M.: A 3-d ultrasound-based framework to characterize the echo morphology of carotid plaques. IEEE Transactions on Biomedical Engineering 56(5) (2009) 1442–1453
[4] Yang, X., Jin, J., He, W., Yuchi, M., Ding, M.: Segmentation of the common carotid artery with active shape models from 3d ultrasound images. In: Medical Imaging 2012: Computer-Aided Diagnosis. Volume 8315., SPIE (2012) 718–725
[5] Ukwatta, E., Awad, J., Ward, A., Buchanan, D., Samarabandu, J., Parraga, G., Fenster, A.: Three-dimensional ultrasound of carotid atherosclerosis: semiautomated segmentation using a level set-based method. Medical hysics 38(5) (2011) 2479–2493
[6] Hossain, M.M., AlMuhanna, K., Zhao, L., Lal, B.K., Sikdar, S.: Semiautomatic segmentation of atherosclerotic carotid artery wall volume using 3d ultrasound imaging. Medical Physics 42(4) (2015) 2029–2043
[7] Chen, D., Zhang, J., Cohen, L.D.: Minimal paths for tubular structure segmentation with coherence penalty and adaptive anisotropy. IEEE Transactions on Image Processing 28(3) (2018) 1271–1284
[8] Menchón-Lara, R.M., Sancho-Gómez, J.L., Bueno-Crespo, A.: Early-stage atherosclerosis detection using deep learning over carotid ultrasound images. Applied Soft Computing 49 (2016) 616–628
[9] Shin, J., Tajbakhsh, N., Hurst, R.T., Kendall, C.B., Liang, J.: Automating carotid intimamedia thickness video interpretation with convolutional neural networks. In: Proceedings of the IEEE conference on Computer Vision and Pattern Recognition. (2016) 2526–2535
[10] Alblas, D., Brune, C., Wolterink, J.M.: Deep-learning-based carotid artery vessel wall segmentation in black-blood mri using anatomical priors. In: Medical Imaging 2022: Image Processing. Volume 12032., SPIE (2022) 237–244
[11] Azzopardi, C., Camilleri, K.P., Hicks, Y.A.: Bimodal automated carotid ultrasound segmentation using geometrically constrained deep neural networks. IEEE Journal of Biomedical and Health Informatics 24(4) (2020) 1004–1015
[12] Fu, J., Liu, J., Tian, H., Li, Y., Bao, Y., Fang, Z., Lu, H.: Dual attention network for scensegmentation. In: roceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. (2019) 3146–3154
[13] 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. (2016) 770–778
[14] Zhao, X., Li, R., Hippe, D.S., Hatsukami, T.S., Yuan, C., Investigators, C.I., et al.: Chinese atherosclerosis risk evaluation (care ii) study: a novel cross-sectional, multicentre study of the prevalence of high-risk atherosclerotic carotid plaque in chinese patients with ischaemic cerebrovascular events—design and rationale. Stroke and vascular neurology 2(1) (2017)
[15] 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 (2015) 234–241
[16] Zhou, Z., Rahman Siddiquee, M.M., Tajbakhsh, N., Liang, J.: Unet++: A nested u-net architecture for medical image segmentation. In: Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support: 4th International Workshop, DLMIA 2018, and 8th International Workshop, ML-CDS 2018, Held in Conjunction with MICCAI 2018, Granada, Spain, September 20, 2018, Proceedings 4, Springer (2018) 3–11
[17] Oktay, O., Schlemper, J., Folgoc, L.L., Lee, M., Heinrich, M., Misawa, K., Mori, K., McDonagh, S., Hammerla, N.Y., Kainz, B., et al.: Attention u-net: Learning where to look for the pancreas. arXiv preprint arXiv:1804.03999 (2018)
[18] Yu, H., Zha, S., Huangfu, Y., Chen, C., Ding, M., Li, J.: Dual attention u-net for multisequence cardiac mr images segmentation. In: Myocardial Pathology Segmentation Combining Multi-Sequence Cardiac Magnetic Resonance Images: First Challenge, MyoPS 2020, Held in Conjunction with MICCAI 2020, Lima, Peru, October 4, 2020, Proceedings 1, Springer (2020) 118–127
[19] Xiao, X., Lian, S., Luo, Z., Li, S.: Weighted res-unet for high-quality retina vessel segmentation. In: 2018 9th international conference on information technology in medicine and education (ITME), IEEE (2018) 327–331
[20] Chen, J., Lu, Y., Yu, Q., Luo, X., Adeli, E., Wang, Y., Lu, L., Yuille, A.L., Zhou, Y.: Transunet: Transformers make strong encoders for medical image segmentation. arXiv preprint arXiv:2102.04306 (2021)
[21] Cao, H., Wang, Y., Chen, J., Jiang, D., Zhang, X., Tian, Q., Wang, M.: Swin-unet: Unet-like pure transformer for medical image segmentation. In: European Conference on Computer Vision, Springer (2022) 205–218
[2] Chen, H., Zhao, X., Dou, J., Du, C., Yang, R., Sun, H., Yu, S., Zhao, H., Yuan, C., Balu, N.: Carotid vessel wall segmentation and atherosclerosis diagnosis challenge(2022). https: //vessel-wall-segmentation-2022.grand-challenge.org/
[3] Seabra, J.C., Pedro, L.M., e Fernandes, J.F., Sanches, J.M.: A 3-d ultrasound-based framework to characterize the echo morphology of carotid plaques. IEEE Transactions on Biomedical Engineering 56(5) (2009) 1442–1453
[4] Yang, X., Jin, J., He, W., Yuchi, M., Ding, M.: Segmentation of the common carotid artery with active shape models from 3d ultrasound images. In: Medical Imaging 2012: Computer-Aided Diagnosis. Volume 8315., SPIE (2012) 718–725
[5] Ukwatta, E., Awad, J., Ward, A., Buchanan, D., Samarabandu, J., Parraga, G., Fenster, A.: Three-dimensional ultrasound of carotid atherosclerosis: semiautomated segmentation using a level set-based method. Medical hysics 38(5) (2011) 2479–2493
[6] Hossain, M.M., AlMuhanna, K., Zhao, L., Lal, B.K., Sikdar, S.: Semiautomatic segmentation of atherosclerotic carotid artery wall volume using 3d ultrasound imaging. Medical Physics 42(4) (2015) 2029–2043
[7] Chen, D., Zhang, J., Cohen, L.D.: Minimal paths for tubular structure segmentation with coherence penalty and adaptive anisotropy. IEEE Transactions on Image Processing 28(3) (2018) 1271–1284
[8] Menchón-Lara, R.M., Sancho-Gómez, J.L., Bueno-Crespo, A.: Early-stage atherosclerosis detection using deep learning over carotid ultrasound images. Applied Soft Computing 49 (2016) 616–628
[9] Shin, J., Tajbakhsh, N., Hurst, R.T., Kendall, C.B., Liang, J.: Automating carotid intimamedia thickness video interpretation with convolutional neural networks. In: Proceedings of the IEEE conference on Computer Vision and Pattern Recognition. (2016) 2526–2535
[10] Alblas, D., Brune, C., Wolterink, J.M.: Deep-learning-based carotid artery vessel wall segmentation in black-blood mri using anatomical priors. In: Medical Imaging 2022: Image Processing. Volume 12032., SPIE (2022) 237–244
[11] Azzopardi, C., Camilleri, K.P., Hicks, Y.A.: Bimodal automated carotid ultrasound segmentation using geometrically constrained deep neural networks. IEEE Journal of Biomedical and Health Informatics 24(4) (2020) 1004–1015
[12] Fu, J., Liu, J., Tian, H., Li, Y., Bao, Y., Fang, Z., Lu, H.: Dual attention network for scensegmentation. In: roceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. (2019) 3146–3154
[13] 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. (2016) 770–778
[14] Zhao, X., Li, R., Hippe, D.S., Hatsukami, T.S., Yuan, C., Investigators, C.I., et al.: Chinese atherosclerosis risk evaluation (care ii) study: a novel cross-sectional, multicentre study of the prevalence of high-risk atherosclerotic carotid plaque in chinese patients with ischaemic cerebrovascular events—design and rationale. Stroke and vascular neurology 2(1) (2017)
[15] 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 (2015) 234–241
[16] Zhou, Z., Rahman Siddiquee, M.M., Tajbakhsh, N., Liang, J.: Unet++: A nested u-net architecture for medical image segmentation. In: Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support: 4th International Workshop, DLMIA 2018, and 8th International Workshop, ML-CDS 2018, Held in Conjunction with MICCAI 2018, Granada, Spain, September 20, 2018, Proceedings 4, Springer (2018) 3–11
[17] Oktay, O., Schlemper, J., Folgoc, L.L., Lee, M., Heinrich, M., Misawa, K., Mori, K., McDonagh, S., Hammerla, N.Y., Kainz, B., et al.: Attention u-net: Learning where to look for the pancreas. arXiv preprint arXiv:1804.03999 (2018)
[18] Yu, H., Zha, S., Huangfu, Y., Chen, C., Ding, M., Li, J.: Dual attention u-net for multisequence cardiac mr images segmentation. In: Myocardial Pathology Segmentation Combining Multi-Sequence Cardiac Magnetic Resonance Images: First Challenge, MyoPS 2020, Held in Conjunction with MICCAI 2020, Lima, Peru, October 4, 2020, Proceedings 1, Springer (2020) 118–127
[19] Xiao, X., Lian, S., Luo, Z., Li, S.: Weighted res-unet for high-quality retina vessel segmentation. In: 2018 9th international conference on information technology in medicine and education (ITME), IEEE (2018) 327–331
[20] Chen, J., Lu, Y., Yu, Q., Luo, X., Adeli, E., Wang, Y., Lu, L., Yuille, A.L., Zhou, Y.: Transunet: Transformers make strong encoders for medical image segmentation. arXiv preprint arXiv:2102.04306 (2021)
[21] Cao, H., Wang, Y., Chen, J., Jiang, D., Zhang, X., Tian, Q., Wang, M.: Swin-unet: Unet-like pure transformer for medical image segmentation. In: European Conference on Computer Vision, Springer (2022) 205–218