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Found 8 papers, 3 papers with code
MedShapeNet -- A Large-Scale Dataset of 3D Medical Shapes for Computer Vision
For the medical domain, we present a large collection of anatomical shapes (e. g., bones, organs, vessels) and 3D models of surgical instrument, called MedShapeNet, created to facilitate the translation of data-driven vision algorithms to medical applications and to adapt SOTA vision algorithms to medical problems.
Joint nnU-Net and Radiomics Approaches for Segmentation and Prognosis of Head and Neck Cancers with PET/CT images
Then 3D nnU-Net architecture was adopted to automatic segmentation of primary tumor and lymph nodes synchronously. Based on predicted segmentation, ten conventional features and 346 standardized radiomics features were extracted for each patient.
Evaluation of importance estimators in deep learning classifiers for Computed Tomography
Interestingly, there was a critical discrepancy between model-centric (fidelity) and human-centric (ROC and DSC) evaluation.
Overview of the HECKTOR Challenge at MICCAI 2021: Automatic Head and Neck Tumor Segmentation and Outcome Prediction in PET/CT Images
The comparison of the PFS prediction performance in Tasks 2 and 3 suggests that providing the GTVt contour was not crucial to achieve best results, which indicates that fully automatic methods can be used.
Squeeze-and-Excitation Normalization for Automated Delineation of Head and Neck Primary Tumors in Combined PET and CT Images
Development of robust and accurate fully automated methods for medical image segmentation is crucial in clinical practice and radiomics studies.
Next Generation Radiogenomics Sequencing for Prediction of EGFR and KRAS Mutation Status in NSCLC Patients Using Multimodal Imaging and Machine Learning Approaches
Conclusion: We demonstrated that radiomic features extracted from different image-feature sets could be used for EGFR and KRAS mutation status prediction in NSCLC patients, and showed that they have more predictive power than conventional imaging parameters.
PET/CT Radiomic Sequencer for Prediction of EGFR and KRAS Mutation Status in NSCLC Patients
The aim of this study was to develop radiomic models using PET/CT radiomic features with different machine learning approaches for finding best predictive epidermal growth factor receptor (EGFR) and Kirsten rat sarcoma viral oncogene (KRAS) mutation status.
Reliability of PET/CT shape and heterogeneity features in functional and morphological components of Non-Small Cell Lung Cancer tumors: a repeatability analysis in a prospective multi-center cohort
Features were more reliable in PET with quantizationB, whereas quantizationW showed better results in CT. Conclusion: The test-retest repeatability of shape and heterogeneity features in PET and low-dose CT varied greatly amongst metrics.
