Medical-image Analysis and Statistical Interpretation (MASI) Lab

Example renderings of (A) BrainCOLOR cortical and sub-cortical labels, (B) Eve cortical and white matter labels and (C) rectified BrainCOLOR and Eve cortical and white matter labels.

Evaluation of Atlas-Based White Matter Segmentation with Eve

Feb. 12, 2015—Andrew J. Plassard, Kendra E. Hinton, Christopher Gonzalez, Vijay Venkatraman, Susan M. Resnick, Bennett A. Landman. “Evaluation of Atlas-Based White Matter Segmentation with Eve” In Proceedings of the SPIE Medical Imaging Conference. Orlando, Florida, February 2015. † Full text: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4405655/ Abstract Multi-atlas labeling has come in wide spread use for whole brain labeling on magnetic...

Intensity profiles and spatial distributions of 12 organs of interest (a–l) and a rendering of a manual segmentation (m). Note that the plotted ranges of the 12 intensity profiles are consistent, i.e., −200 ~ 400 Hounsfield unit (HU).

Multi-Atlas Segmentation for Abdominal Organs with Gaussian Mixture Models

Feb. 12, 2015—Ryan P. Burke, Zhoubing Xu, Christopher P. Lee, Rebeccah Baucom, Benjamin K. Poulose, Richard G. Abramson, Bennett A. Landman. “Multi-Atlas Segmentation for Abdominal Organs with Gaussian Mixture Models.” In Proceedings of the SPIE Medical Imaging Conference. Orlando, Florida, February 2015. Full text: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4405670/ Abstract Abdominal organ segmentation with clinically acquired computed tomography (CT) is drawing...

Comparison of the ON of a healthy control (A) and an MS patient with severe ON atrophy (B). Red lines in the axial images show the location of the coronal slices to the right.

“Constructing a statistical atlas of the radii of the optic nerve and cerebrospinal fluid sheath in young healthy adults.

Feb. 12, 2015—Robert L Harrigan, Andrew J. Plassard, Louise A. Mawn, Robert L. Galloway, Seth A. Smith, Bennett A. Landman. “Constructing a statistical atlas of the radii of the optic nerve and cerebrospinal fluid sheath in young healthy adults.” In Proceedings of the SPIE Medical Imaging Conference. Orlando, Florida, February 2015. † Full text: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4405797/ Abstract Optic...

Flowchart of the ImageNet approach applied the the anonylized imaging data from clinical radiology.

Toward content based image retrieval with deep convolutional neural networks

Feb. 12, 2015—Judah E. Sklan, Andrew J. Plassard, Daniel Fabbri, Bennett A. Landman. “Toward content based image retrieval with deep convolutional neural networks ” In Proceedings of the SPIE Medical Imaging Conference. Orlando, Florida, February 2015. † Full text: https://www.ncbi.nlm.nih.gov/pubmed/?term=%E2%80%9CToward+content+based+image+retrieval+with+deep+convolutional+neural+networks+%E2%80%9D Abstract Content–based image retrieval (CBIR) offers the potential to identify similar case histories, understand rare disorders, and...

Revealing Latent Value of Clinically Acquired CTs of Traumatic Brain Injury Through Multi-Atlas Segmentation in a Retrospective Study of 1,003 with External Cross-Validation

Feb. 1, 2015—Andrew J. Plassard, Patrick D. Kelly, Andrew J. Asman, Hakmook Kang, Mayur B. Patel, Bennett A. Landman. “Revealing Latent Value of Clinically Acquired CTs of Traumatic Brain Injury Through Multi-Atlas Segmentation in a Retrospective Study of 1,003 with External Cross-Validation” In Proceedings of the SPIE Medical Imaging Conference. Orlando, Florida, February 2015. Full text: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4405676/...

Qualitative results for the optimal multi-atlas segmentation approach for seven subjects are shown. For a typical subject, the top rows compare manual and automatic results for a representative 2-D slice. The bottom rows show pointwise surface distance error of the label fusion estimate for the ONs and the eye globe structure. The proposed multi-atlas pipeline results in reasonably accurate segmentations for the ON structure. However, slight oversegmentations of the ONs can be observed in certain cases (subjects 4 and 7) supporting the results in the volumetry section

Robust Optic Nerve Segmentation on Clinically Acquired CT

Dec. 31, 2014—Robert Harrigan, Swetasudha Panda, Andrew J. Asman, Michael P. DeLisi, Benjamin C. W. Yvernault, Seth A. Smith, Robert L. Galloway, Louise A. Mawn, Bennett A. Landman “Robust Optic Nerve Segmentation on Clinically Acquired CT.” Journal of Medical Imaging. 1(3), 034006 (Dec 17, 2014). † PMC3903299 Full Text: https://www.ncbi.nlm.nih.gov/pubmed/26158064 Abstract The optic nerve (ON) plays a...

Overview of the need for accurate estimates in high performance medical imaging. Complex multi-stage imaging layouts need to provide accurate estimations of their resource requirements in order to leverage high performance computing resources. The upper-right inlay shows histograms of the runtime for a single software module (“File Collection Efficient Registration”) on datasets acquired on 21 individuals

Resource Estimation in High Performance Medical Image Computing

Oct. 31, 2014—Rueben Banalagay, Kelsie J. Covington, D.Mitch Wilkes, Bennett A. Landman. “Resource Estimation in High Performance Medical Image Computing.” Neuroinformatics. 2014 Oct;12(4):563-73. † PMC4381797 Full Text: https://www.ncbi.nlm.nih.gov/pubmed/24906466 Abstract Medical imaging analysis processes often involve the concatenation of many steps (e.g., multi-stage scripts) to integrate and realize advancements from image acquisition, image processing, and computational analysis. With the...

DICOM can be converted into research file formats viewed with SPM[7], MIPAV[8]. 3D Slicer[9]. MRIcron[10], MATLAB (Mathworks, Natick, MA)

Validating DICOM transcoding with an open multi-format resource

Oct. 31, 2014—Benjamin C. Yvernault, Charles D. Theobald, Jr., Jolinda C. Smith, Victoria Villalta, David H. Zald, Bennett A. Landman. “Validating DICOM transcoding with an open multi-format resource.” Neuroinformatics. 2014 Oct;12(4):615-7. † PMC4391369 Full Text: https://www.ncbi.nlm.nih.gov/pubmed/24777387 Abstract The Digital Imaging and Communications in Medicine (DICOM) standard has allowed wide-scale interoperability between medical imaging devices allowed for construction...

Flowchart of the proposed method. Given registered atlases with variable qualities, atlas selection and statistical fusion are considered as two necessary steps to obtain a reasonable fusion estimate of the target segmentation. The SIMPLE algorithm implicitly combines these two steps to fusion selected atlases; however, more information can be incorporated to improve the atlas segmentation, and a more advanced fusion technique can be used after the atlases are selected. We propose to (1) extract a probabilistic prior of the target segmentation by context learning to regularize the atlas selection in SIMPLE and (2) use Joint Label Fusion to obtain the final segmentation while characterizing of the correlated errors from among atlases.

SIMPLE Is a Good Idea (and Better with Context Learning)

Sep. 30, 2014—Zhoubing Xu, Andrew J. Asman, Peter L. Shanahan, Richard G. Abramson, Bennett A. Landman. “SIMPLE Is a Good Idea (and Better with Context Learning)”, In International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI), Boston, MA, September 2014. Full text: PubMed Abstract Selective and iterative method for performance level estimation (SIMPLE) is a multi-atlas segmentation technique that integrates...

Acquired images (left) and manual labels (right) are shown for two controls (a, b), a multiple sclerosis patient (c), and an optic nerve head drusen patient (d). Axial, sagittal, and coronal views of the manually labeled optic nerves, optic chiasm, and eye globes for the subject in (b) are shown in (e).

Evaluation of Multi-Atlas Label Fusion for Orbital Segmentation on In Vivo MRI

Jul. 31, 2014—Swetasudha Panda, Andrew J Asman, Shweta P Khare, Lindsey Thompson, Louise A Mawn, Seth A Smith, Bennett A Landman. “Evaluation of Multi-Atlas Label Fusion for Orbital Segmentation on In Vivo MRI.” Journal of Medical Imaging. 1(2), 024002 (Jul–Sep 2014) † PMC4280790 Full Text: https://www.ncbi.nlm.nih.gov/pubmed/25558466 Abstract Multi-atlas methods have been successful for brain segmentation, but their application...

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