Weakly Supervised Medical Image Segmentation With Soft Labels and Noise Robust Loss

no code implementations • 16 Sep 2022 • Banafshe Felfeliyan, Abhilash Hareendranathan, Gregor Kuntze, Stephanie Wichuk, Nils D. Forkert, Jacob L. Jaremko, Janet L. Ronsky

The aim of this paper was to develop and evaluate a method to generate probabilistic labels based on multi-rater annotations and anatomical knowledge of the lesion features in MRI and a method to train segmentation models using probabilistic labels using normalized active-passive loss as a "noise-tolerant loss" function.

Image Segmentation Medical Diagnosis +3

Self-Supervised-RCNN for Medical Image Segmentation with Limited Data Annotation

no code implementations • 17 Jul 2022 • Banafshe Felfeliyan, Abhilash Hareendranathan, Gregor Kuntze, David Cornell, Nils D. Forkert, Jacob L. Jaremko, Janet L. Ronsky

The effectiveness of the proposed method for segmentation tasks in different pre-training and fine-tuning scenarios is evaluated based on the Osteoarthritis Initiative dataset.

Anomaly Detection Image Segmentation +4

Improved-Mask R-CNN: Towards an Accurate Generic MSK MRI instance segmentation platform (Data from the Osteoarthritis Initiative)

no code implementations • 27 Jul 2021 • Banafshe Felfeliyan, Abhilash Hareendranathan, Gregor Kuntze, Jacob L. Jaremko, Janet L. Ronsky

The iMaskRCNN led to improved bone and cartilage segmentation compared to Mask RCNN as indicated with the increase in dice score from 95% to 98% for the femur, 95% to 97% for tibia, 71% to 80% for femoral cartilage, and 81% to 82% for tibial cartilage.

Instance Segmentation Segmentation +1

Modelling Errors in X-ray Fluoroscopic Imaging Systems Using Photogrammetric Bundle Adjustment With a Data-Driven Self-Calibration Approach

no code implementations • 29 Sep 2018 • Jacky C. K. Chow, Derek Lichti, Kathleen Ang, Gregor Kuntze, Gulshan Sharma, Janet Ronsky

X-ray imaging is a fundamental tool of routine clinical diagnosis.

3D Reconstruction