王进科

姓名

王进科

性别

男

出生年月

1983.01

导师类别

专业学位硕导

技术职称

教授

任职部门

荣成学院

E-mail

19598985@qq.com

电话

13863132787

教育经历

2002.09-2006.07，山东大学，软件工程专业，工学学士

2006.09-2009.04，山东大学，计算机应用专业，工学硕士

2011.03-2016.07，哈尔滨工业大学，机械电子工程专业，工学博士

2017.09-2019.09，大阪大学，医学部放射线研究科专攻，博士后

工作经历

2009.07-2011.09，哈尔滨理工大学，荣成学院，助教

2011.09-2018.09，哈尔滨理工大学，荣成学院，讲师

2018.09-2023-08，哈尔滨理工大学，荣成学院，副教授

2019.10-至今，哈尔滨理工大学，自动化学院，专业学位硕导

2019.10-至今，哈尔滨理工大学，荣成学院，教学督导

2023.05-至今，哈尔滨理工大学，荣成学院，学术分委会委员

2023.09-至今，哈尔滨理工大学，荣成学院，教授

研究领域及方向

1.研究领域（学科）：

[1] 模式识别与人工智能

[2] 计算机视觉

2.主要研究方向：

[1] 膝关节软骨MRI图像的处理与分析

[2] 眼底视网膜图像的处理与分析

[3] 腹部、肺部CT图像的处理与分析

3.个人简介：

本人长期从事基于深度学习与机器学习的医学图像的自动处理研究工作，欢迎自律自制，热衷AI的同学加入本人所在的医学图像处理团队（https://402-lab.github.io/）

科研项目

1. 主持或参与纵向课题

[1] 2018，主持国家自然科学基金，用于活体肝移植术前评估的肝脏CT图像自动分割方法研究

[2] 2010，参与国家自然科学基金，用于准确诊断膝关节炎病情发展的MR图像配准研究

[3] 2015，参与国家自然科学基金，MR图像监测骨关节炎病情变化的量化研究

[4] 2024，主持黑龙江省自然科学基金，膝骨关节置换术前个性化假体设计的影像自动分割与配准方法研究.

[4] 2019，主持黑龙江省自然科学基金，含病变肝脏CT的自动分割关键技术

[5] 2016，主持黑龙江省自然科学基金，基MR图像精确配准技术监测膝关节软骨厚度变化

[6] 2015，参与黑龙江省自然科学基金，基于扩散光断层成像的骨关节炎无创检测

[7] 2017，主持黑龙江省创新人才培养计划，CT图像中肝脏的自动分割方法研究.

[8] 2017，主持哈尔滨市科技创新人才项目，基于MR图像精准分割技术的膝关节软骨厚度监测研究.

[9] 2016，主持山东省高等学校科技计划，面向膝关节炎病情监测的医学图像分割与配准研究.

[10] 2015，主持黑龙江省教育厅科学技术研究项目，基于MR图像的膝关节软骨厚度监测技术研究

[11] 2019，主持理工英才杰出青年项目，基于树模型与图谱模型的肝脏CT计算机辅助定位与分割技术

[12] 2023，主持拔尖创新团队培养计划，腹部CT的多器官自动分类识别关键技术

代表性科研论文

[1] Wang J, Li X, Cheng Y. (2023) Towards an extended EfficientNet-based U-Net framework for joint optic disc and cup segmentation in the fundus image[J]. Biomedical Signal Processing and Control, 2023, 85: 104906.

[2] Wang J, Zhou L, Yuan Z, et al.(2023) MIC-Net: multi-scale integrated context network for automatic retinal vessel segmentation in fundus image[J]. Mathematical Biosciences and Engineering, 2023, 20(4): 6912-6931.

[3] Wang J, Zhang X, Guo L, et al. Multi-scale attention and deep supervision-based 3D UNet for automatic liver segmentation from CT[J]. Mathematical biosciences and engineering: MBE, 2023, 20(1): 1297-1316.

[4] Wang, J., Lv, P., Wang, H., & Shi, C. (2021). SAR-U-Net: Squeeze-and-excitation block and atrous spatial pyramid pooling based residual U-Net for automatic liver segmentation in Computed Tomography. Computer Methods and Programs in Biomedicine, 208, 106268.

[5] Lv, P., Wang, J., & Wang, H. (2022). 2.5 D lightweight RIU-Net for automatic liver and tumor segmentation from CT. Biomedical Signal Processing and Control, 75, 103567.

[6] Lv, P., Wang, J., Zhang, X., & Shi, C. (2022). Deep supervision and atrous inception-based U-Net combining CRF for automatic liver segmentation from CT. Scientific Reports, 12(1), 16995.

[7] Wang, J., Zhang, X., Lv, P., Wang, H., & Cheng, Y. (2022). Automatic liver segmentation using EfficientNet and Attention-based residual U-Net in CT. Journal of Digital Imaging, 1-15.

[8] Lv, P., Wang, J., Zhang, X., Ji, C., Zhou, L., & Wang, H. (2022). An improved residual U-Net with morphological-based loss function for automatic liver segmentation in computed tomography. Math. Biosci. Eng, 19, 1426-1447.

[9] Wang, J., Li, X., Lv, P., & Shi, C. (2021). SERR-U-Net: squeeze-and-excitation residual and recurrent block-based U-Net for automatic vessel segmentation in retinal image. Computational and Mathematical Methods in Medicine, 2021.

[10] Jiang, J., Guo, Y., Bi, Z., Huang, Z., Yu, G., & Wang, J. (2022). Segmentation of prostate ultrasound images: the state of the art and the future directions of segmentation algorithms. Artificial Intelligence Review, 1-37.

[11] Yao, D., Zhan, X., Zhan, X., Kwoh, C. K., Li, P., & Wang, J. (2020). A random forest based computational model for predicting novel lncRNA-disease associations. BMC bioinformatics, 21, 1-18.

[12] Shi, C., Cheng, Y., Wang, J., Wang, Y., Mori, K., & Tamura, S. (2017). Low-rank and sparse decomposition based shape model and probabilistic atlas for automatic pathological organ segmentation. Medical image analysis, 38, 30-49.

[13] Wang, J., Cheng, Y., Guo, C., Wang, Y., & Tamura, S. (2016). Shape–intensity prior level set combining probabilistic atlas and probability map constrains for automatic liver segmentation from abdominal CT images. International journal of computer assisted radiology and surgery, 11, 817-826.

[14] Wang, J., & Guo, H. (2016). Automatic approach for lung segmentation with juxta-pleural nodules from thoracic CT based on contour tracing and correction. Computational and mathematical methods in medicine, 2016.

[15] Wang, J., & Shi, C. (2017). Automatic construction of statistical shape models using deformable simplex meshes with vector field convolution energy. Biomedical engineering online, 16, 1-19.

[16] Guo, H., Song, S., Wang, J., Guo, M., Cheng, Y., Wang, Y., & Tamura, S. (2018). 3D surface voxel tracing corrector for accurate bone segmentation. International journal of computer assisted radiology and surgery, 13, 1549-1563.

[17] Wang, J., Zu, H., Guo, H., Bi, R., Cheng, Y., & Tamura, S. (2019). Patient-specific probabilistic atlas combining modified distance regularized level set for automatic liver segmentation in CT. Computer Assisted Surgery, 24, 20-26.

[18] Guo, C., Cheng, Y., Guo, H., Wang, J., Wang, Y., & Tamura, S. (2015). Surface-based rigid registration using a global optimization algorithm for assessment of MRI knee cartilage thickness changes. Biomedical Signal Processing and Control, 18, 303-316.

[19] Shi C, Cheng Y, Wang J, et al. Low-rank and sparse decomposition based shape model and probabilistic atlas for automatic pathological organ segmentation[J]. Medical image analysis, 2017, 38: 30-49.

[20] Jiang, J., Guo, Y... Wang J, et al. Segmentation of prostate ultrasound images: the state of the art and the future directions of segmentation algorithms. Artificial Intelligence Review，2022.

[21] Yuan Z, Wang J, Xu Y, et al. (2024) CC-TransXNet: a hybrid CNN-transformer network for automatic segmentation of optic cup and optic disk from fundus images[J]. Medical & Biological Engineering & Computing, 2025, 63(4): 1027-1044.

[22] Sun Y, Xu T, Wang J, et al. (2025) DST-Net: Dual self-integrated transformer network for semi-supervised segmentation of optic disc and optic cup in fundus image[J]. Electronic Research Archive, 33(4).

代表性专利、专著

[1]王进科. 《医学CT图像自动分割关键技术》. 电子科技大学出版社. 2024.11

[2]王进科, 徐天泽. 哈尔滨理工大学威海研究院.一种眼底成像系统: 2024-10-22.

[3]王进科, 王庆.哈尔滨理工大学威海研究院.一种内窥镜图像处理系统:2024-09-27.

[4] 王进科, 李鹏胜.哈尔滨理工大学威海研究院.一种医疗图像采集系统:.2024-09-24.

[5]王进科, 王振友.哈尔滨理工大学威海研究院.一种升降平台.2024-01-30.

[6]王进科, 郭良.哈尔滨理工大学威海研究院.一种视觉图像处理的摄像装.2023-06-09.

[7] 王进科, 杨志鹏.哈尔滨理工大学.一种用于人工智能图像的检测装置.2022-05-10.

教学科研获奖

[1] 2020，第二届荣成市青年科技奖

[2] 2018，山东省高等学校科学技术三等奖

社会、学会及学术兼职

[1] 《Biomedical Signal Processing and Control》、《International Journal of Computer Assisted Radiology and Surgery》、《Engineering Applications of Artificial Intelligence 》、《生物医学工程学报》等国内外期刊审稿人、ISIS系统评议专家、中国国际创新大赛评审专家。

荣誉奖励

[1] 2010，哈尔滨理工大学优秀教师

[2] 2013，哈尔滨理工大学优秀毕业设计指导教师

[3] 2016，哈尔滨理工大学优秀主讲教师