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Automated Lesion Quantification in Positron Emission Tomography (PET) Images

68Ga-DOTATATE PET has demonstrated the highest accuracy in detection and staging of gastroenteropancreatic neuroendocrine tumors (GEP-NETs). It is critical to quantify residual 68Ga DOTATATE positive disease burden for therapy development. The central goal of this project is to develop an automated, generelizable lesion detection for livers in 68Ga-DOTATATE PET images.





Automated Liver Lesion Detection in 68Ga DOTATATE PET/CT Using a Deep Fully Convolutional Neural Network
J. Wehrend, M. Silosky, F. Xing, B. Chin
EJNMMI Research, 11(98): 1-11, 2021


Automated Liver Lesion Detection in 68Ga DOTATATE PET/CT: Preliminary Results using a Deep Learning 3D Fully Convolutional Network
B. Chin, J. Wehrend, M. Silosky, C. Halley, R. Niman, K. Moses, R. Karki, F. Xing
Journal of Nuclear Medicine, 62 (supplement 1):1184, 2021


Feasibility of List Mode Reconstructions of 68DOTATATE PET/CT to Predict Contrast-to-Noise Ratio of Hepatic Metastases in Shorter Acquisition PET Reconstructions
Accepted to SNMMI 2022 Annual Meeting, 2022








KiNet: A Computerized Method for Ki67 Labeling Index Assessment

The central goal of this project is to develop a computerized method, namely KiNet, for Ki67 labeling (LI) index assessment in Ki67 immohistochemistry stained images of gastrointestinal and pancreatic neuroendocrine tumors (NETs). We use deep neural networks to implement an end-to-end learning method to identify different types of nuclei (i.e., immunopositive tumor, immunonegative tumor and non-tumor) for Ki67 LI measurement.

Source codes: [KiNet_v1]





Pixel-to-Pixel Learning with Weak Supervision for Single-stage Nucleus Recognition in Ki67 Images
Xing et al., IEEE Transactions on Biomedical Engineering, vol. 66, no. 11, pp. 3088-3097, 2019


Adversarial Domain Adaptation and Pseudo-Labeling for Cross-Modality Microscopy Image Quantification
Xing et al., The 22nd International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI), vol. 11764, pp.740-749, 2019


Generative Adversarial Domain Adaptation for Nucleus Quantification in Images of Tissue Immunohistochemically Stained for Ki-67
Zhang et al., JCO Clinical Cancer Informatics, vol. 4, pp.666-679, 2020 [Codes]


Bidirectional Mapping-Based Domain Adaptation for Nucleus Detection in Cross-Modality Microscopy Images
Xing et al., IEEE Transactions on Medical Imaging, vol. 40, no 12, pp. 2880-2896, 2021





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