Labeling Category

Representative images are predicted to associate with comorbidities and ICD-10 codes
(phenotype components) identified in each risk category. The red outlines show the pancreas
tissue can be different under phenotyping contexts. (1) is from a nominally healthy pancreas
group with potential lung infections; (2) is from type I diabetes and other chronic kidney disease
patients with atrophic pancreas; (3) is from other metabolic syndromes including type II diabetes;
(4) is from patients with weight loss and pancreatitis.

Pancreas CT Segmentation by Predictive Phenotyping

Dec. 14, 2021—Y. Tang, R.Gao, H.H.Lee, Q.Yang, X.Yu,Y.Zhou, S.Bao, Y.Huo, J.Spraggins, J.Virostko, Z.Xu, B.A. Landman. “Pancreas CTSegmentation by Predictive Phenotyping”. International Conference on MedicalImage Computing and Computer Assisted Intervention(MICCAI), 2021 Full Text: https://link.springer.com/chapter/10.1007/978-3-030-87193-2_3 Abstract Pancreas CT segmentation offers promise at understanding the structural manifestation of metabolic conditions. To date, the medical primary record of conditions that impact...

Average shape and position of all bundles that are part of the TractEM protocol (association and projection pathways are shown only for the left hemisphere). The average was computed using segmentation from 4 raters and 4 datasets (BLSA) using a majority vote. This highlights the general agreement on the shape, but as seen in the lower right vignette the variability of each individual segmentation can be quite extreme.

TractEM: Evaluation of protocols for deterministic tractography white matter atlas

Dec. 3, 2021—Rheault, Francois, Roza G. Bayrak, Xuan Wang, Kurt G. Schilling, Jasmine M. Greer, Colin B. Hansen, Cailey Kerley et al. “TractEM: Evaluation of protocols for deterministic tractography white matter atlas.” Magnetic Resonance Imaging 85 (2022): 44-56. Full Text Abstract Purpose: One of the key challenges of the manual delineation of white matter pathways is human-rater’s subjectivity in labeling....

Labeling Lateral Prefrontal Sulci using Spherical Data Augmentation and Context-aware Training

Apr. 1, 2021—Ilwoo Lyu, Shunxing Bao, Lingyan Hao, Jewelia Yao, Jacob Miller, Willa Voorhies, Warren Taylor, Silvia Bunge, Kevin Weiner, Bennett Landman. “Labeling Lateral Prefrontal Sulci using Spherical Data Augmentation and Context-aware Training”. NeuroImage, 229, 117758, 2021. [Full text][Code] Abstract The inference of cortical sulcal labels often focuses on deep (primary and secondary) sulcal regions, whereas shallow...

Generalizing deep whole-brain segmentation for post-contrast MRI with transfer learning

Dec. 26, 2020—Bermudez, C., Remedios, S. W., Ramadass, K., McHugo, M., Heckers, S., Huo, Y., & Landman, B. A. (2020). Generalizing deep whole-brain segmentation for post-contrast MRI with transfer learning. Journal of Medical Imaging, 7(6), 064004. Full Text: https://pubmed.ncbi.nlm.nih.gov/33381612/ Abstract Purpose: Generalizability is an important problem in deep neural networks, especially with variability of data acquisition in...

Automatic Labeling of Cortical Sulci using Convolutional Neural Networks in a Developmental Cohort

Apr. 20, 2020—Lingyan Hao, Shunxing Bao, Yucheng Tang, Riqiang Gao, Prasanna Parvathaneni, Jacob Miller, Willa Voorhies, Jewelia Yao, Silvia Bunge, Kevin Weiner, Bennett Landman, Ilwoo Lyu. “Automatic Labeling of Cortical Sulci using Convolutional Neural Networks in a Developmental Cohort”. IEEE International Symposium on Biomedical Imaging (ISBI) 2020, IEEE, 412-415, Iowa City, Iowa, USA, 2020. [Full text][Code] Abstract...

With 6 raters hired to identify 82 RV insertion points in 41 randomly selected pig heart slices, the analyzed results of 3 of the slices are shown, where the red “x” are all rater decisions, and the green “o” show the MAP Continuous STAPLE fusion comparing to an expert's decision (yellow “x”). In the first image, the fusion corrects the one rater's mistake that has deviated too much. In the second image, fusion brings the result closer to the expert decision than the scattered rater decisions. In the last image, although rater deviations cause the fusion also to deviate, it is still close to the expert decision.

Statistical Fusion of Continuous Labels: Identification of Cardiac Landmarks

Feb. 1, 2011—Fangxu Xing, Sahar Soleimanifard, Jerry L. Prince, Bennett A. Landman. “Statistical Fusion of Continuous Labels: Identification of Cardiac Landmarks”, In Proceedings of the SPIE Medical Imaging Conference. Lake Buena Vista, Florida, February 2011 (Oral Presentation) PMC3110005 † Full text: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3110005/ Abstract Image labeling is an essential task for evaluating and analyzing morphometric features in medical...

The three major tasks presented in this paper and the gamut of their observations. The pig cardiac data can be seen in A), the simulated cylinder data can be seen in B), and the sagittal, axial, and coronal cerebellum data can be seen in C). The range of observations is broken up into three classifications. The good classification represents observations that are high quality observations given the original image slice. The bad classification represents observations where the rules were followed but the labeled images are not necessarily close to the ground truth. The ugly classification represents blatant rule breaking and observations that are completely inconsistent with the expected ground truth.

Foibles, Follies, and Fusion: Assessment of Statistical Label Fusion Techniques for Web-Based Collaborations using Minimal Training

Feb. 1, 2011—Andrew J. Asman, Andrew G. Scoggins, Jerry L. Prince, Bennett A. Landman. “Foibles, Follies, and Fusion: Assessment of Statistical Label Fusion Techniques for Web-Based Collaborations using Minimal Training”, In Proceedings of the SPIE Medical Imaging Conference. Lake Buena Vista, Florida, February 2011 PMC3083117† Full text: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3083117/ Abstract Labeling or parcellation of structures of interest on...

Representative labeling results. For illustrative purposes, we show the range of observations divided into visually good classification (generally precise), bad classification (rules were followed but the labeled images are not visually close to the truth), and ugly classification (inconsistent with the expected ground truth). All qualities of observations were observed using all input devices.

Characterizing and Optimizing Rater Performance for Internet-based Collaborative Labeling

Feb. 1, 2011—Joshua A. Stein, Andrew J. Asman, Bennett A. Landman. “Characterizing and Optimizing Rater Performance for Internet-based Collaborative Labeling”, In Proceedings of the SPIE Medical Imaging Conference. Lake Buena Vista, Florida, February 2011 (Oral Presentation) Full text: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3157950/ Abstract Labeling structures on medical images is crucial in determining clinically relevant correlations with morphometric and volumetric features....