Medical Image Processing
ACEnet: Anatomical context-encoding network for neuroanatomy segmentation
For more details on ACEnet, read the article here.
This package contains deep learning tools for segmenting brain structures, as described in the following papers:
- Yuemeng Li, Hongming Li, and Yong Fan. ACEnet: Anatomical context-encoding network for neuroanatomy segmentation. Med Image Anal. 2021 May;70:101991. doi: 10.1016/j.media.2021.101991. Epub 2021 Feb 7. PMID: 33607514; PMCID: PMC8044013.
- Yuemeng Li, Hongming Li, and Yong Fan. ACEnet: Anatomical Context-Encoding Network for Neuroanatomy Segmentation. https://doi.org/10.48550/arxiv.2002.05773, 2020
Automatic kidney segmentation in ultrasound images
This package contains deep learning tools for segmenting kidneys in ultrasound images, as described in the following papers:
- Shi Yin, Qinmu Peng, Hongming Li, Zhengqiang Zhang, Xinge You, Katherine Fischer, Susan L. Furth, Gregory E. Tasian, and Yong Fan. Automatic kidney segmentation in ultrasound images using subsequent boundary distance regression and pixelwise classification networks. Med Image Anal. 2020 Feb;60:101602. doi: 10.1016/j.media.2019.101602. Epub 2019 Nov 8. PMID: 31760193; PMCID: PMC6980346.
- Shi Yin, Qinmu Peng, Hongming Li, Zhengqiang Zhang, Xinge You, Katherine Fischer, Susan L. Furth, Gregory E. Tasian, and Yong Fan. Computer-Aided Diagnosis of Congenital Abnormalities of the Kidney and Urinary Tract in Children Using a Multi-Instance Deep Learning Method Based on Ultrasound Imaging Data. Proc IEEE Int Symp Biomed Imaging. 2020 Apr;2020:1347-1350. doi: 10.1109/isbi45749.2020.9098506. Epub 2020 May 22. PMID: 33850604; PMCID: PMC8040672.
DeepSEED-3D-ConvNets-for-Pulmonary-Nodule-Detection
This package contains deep learning tools for detecting pulmonary nodules, as described in the following paper:
- Li Y, Fan Y. DeepSEED: 3D Squeeze-and-Excitation Encoder-Decoder Convolutional Neural Networks for Pulmonary Nodule Detection. Proc IEEE Int Symp Biomed Imaging. 2020 Apr;2020:1866-1869. doi: 10.1109/ISBI45749.2020.9098317. Epub 2020 May 22. PMID: 33250956; PMCID: PMC7690332.
Local label learning (LLL) for multi-atlas based image segmentation
NITRC: Local Label Learning for Image Segmentation: Tool/Resource Info
This package contains standalone Executable files (linux) and research source codes of the Local label learning (LLL) for multi-atlas based image segmentation methods, described in the following papers:
- Y. Hao, J. Liu, Y. Duan, X. Zhang, C. Yu, T. Jiang, and Y. Fan, "Local label learning (L3) for multi-atlas based segmentation," in SPIE Medical Imaging, 2012, p. 83142E.
- Y. Hao, T. Jiang, and Y. Fan, "Shape-constrained multi-atlas based segmentation with multichannel registration," in Proceeding of SPIE Medical Imaging: Image Processing, vol. 8314, p. 83143N, 2012.
- Y. Hao, T. Jiang, and Y. Fan, "Iterative multi-atlas based segmentation with multi-channel image registration and Jackknife Context Model," in 2012 9th IEEE International Symposium on Biomedical Imaging (ISBI), 2012, pp. 900- 903.
- Y. Hao, T. Wang, X. Zhang, Y. Duan, C. Yu, T. Jiang, and Y. Fan, "Local label learning (LLL) for subcortical structure segmentation: application to hippocampus segmentation," Human Brain Mapping, vol. 35, pp. 2674-2697, 2014.
- H. Zhu, H. Cheng, and Y. Fan, "Random local binary pattern based label learning for multi- atlas segmentation," in Processing of SPIE Medical Imaging: Image Processing, vol. 9413, p. 94131B, 2015.
- H. Zhu, H. Cheng, X. Yang, and Y. Fan, "Metric learning for label fusion in multi-atlas based image segmentation," in 2016 IEEE 13th International Symposium on Biomedical Imaging (ISBI), 2016, pp. 1338-1341.
- H. Zhu, H. Cheng, X. Yang, and Y. Fan, "Metric learning for multi-atlas based segmentation of hippocampus," Neuroinformatics, vol. 15, pp. 41-50, 2017.
- Q. Zheng and Y. Fan, "Integrating semi-supervised label propagation and random forests for multi-atlas based hippocampus segmentation," IEEE International Symposium on Biomedical Imaging (ISBI), April 2018