LOGISMOS segmentation framework (Layered Optimal Graph Image Segmentation for Multiple Objects and Surfaces) facilitates highly efficient multi-dimensional, multi-layered, and multi-object optimum graph-based segmentation and surface editing on image data from various modalities (CT, MR, Ultrasound, OCT, etc.).

LOGISMOS - General approach to 3D, 4D, n-D image segmentation

Sonka team

1. Pre-segmentation that yields an approximate segmentation of the objects of interest and gives information about the topological structures of the target objects;
2. Mesh generation used to specify the structure of a graph G_B, called base graph that defines the neighboring relations among voxels on the sought (optimal) surfaces;
3. Graph construction yielding a weighted directed graph G in which each graph column corresponds to a list of vertices in G. The vertex costs of G can encode edge-based, region-based, and shape-based cost functions. Graph topology allows encoding of multi-surface/multi-object interactions;
4. Graph search yields optimal surfaces corresponding to structures of interest. The sought optimal surfaces are obtained by searching for an optimal closed set in G using efficient maximum flow graph-theory algorithms;
5. Highly efficient “Just-Enough Interaction” strategy for correction of local/regional mis-segmentations as/if needed.

• Application areas:
  • Cardiology
  • Ophthalmology
  • Orthopaedics
  • Neuroscience
  • Pulmonology
  • Radiation oncology
  • Surgery

• Publications
  • Seminal publication - Optimal Surface Segmentation in Volumetric Images—A Graph-Theoretic Approach (2006)
  • PubMed publications of the team - LOGISMOS
  • PubMed publications of the team - Graph based

• Code, library, application sharing
  • Multi-layer 3D human macular and ONH OCT LOGISMOS+JEI segmentation entitled Iowa Reference Algorithms and murine macular segmentation programs are available as downloads for research use at www.iibi.uiowa.edu/downloads, the name Iowa Reference Algorithm is used since it allows analysis of OCT images from scanners of five tested manufacturers.
  • Source code of Alpha Path-Moves is shared at github.com/sydbarrett/AlphaPathMoves
  • Developed with partial support from U01 CA140206 by R. Beichel's group, cancer-related PET image analysis modules (code, libraries, apps) for the popular 3D Slicer environment are shared at github.com/QIICR/PETTumorSegmentation. Full source code is publicly available, is free for use, and shows how to implement a graph-based segmentation method with JEI functionality.

• Results of the LOGISMOS project were and are used in 12 NIH (R01 EY018853, R01 EY019112, R01 HL111453, R01 CA166703, R01 NS094456, R01 EY023279, U01 CA140206, U10 EY017281, U24 CA180918, R21 AR054015, R42 HL108469, K25 CA123112), 5 Veterans Administration (1IK2RX000728, C6810-C, I01 RX001786, CENC0056P, VA-Rehab-R&D-Iowa), and 5 international research projects (Austrian Christian Doppler Soc. project OPTIMA; Ministry of Health of the Czech Republic (16-27465A, 16-28525A, 17-28784A and IKEM (MH-CZ-DRO-IKEMIN 00023001); as well as in FDA-labeled products (VIDA Diagnostics; Medical Imaging Applications LLC).

Image Gallery

Left-to-right: Aortic CT, MR, CTA, MRA image analysis in 3D, 4D; Quantitative results of aortic analysis; 4D cardiac ultrasound; 4D cardiac ultrasound analysis.

Broad applicability of LOGISMOS n-D segmentation methods. Left-to-right: 3D liver tumor segmentation; 3D lung tumor segmentation; Prostate-bladder -- simultaneous segmentation of 2 objects in 3D CT; 3D cartilage MR segmentation -- normal and osteoarthritic joints, note cartilage thinning and ``holes'' in the right-most image.

Left-to-right: Motion artifact reduction with structure awareness in 4D CT (original images with artifacts on the left; Human macular 3D OCT - multi-layer 3D segmentation; Human peripapillary 3D OCT -- layer segmentation and 3D visualization of retinal layers around the optic nerve head; Multi-layer 3D segmentation of murine retinal OCT; First-ever 3D segmentation of human choroidal vasculature from retinal OCT using pilot implementation of the surface-and-region graph search.

Advancement of LOGISMOS flexibility. Left-to-right: 3D segmentation of human choroidal vasculature from retinal OCT using surface-and-region graph search; Simultaneous segmentation of hierarchically-structured interacting objects based on Alpha Path-Moves (ribcage+spine, liver, heart, left kidney); Knee MR segmentation (for longitudinal 4D analyses, central subplate shown); Caudate and putamen segmentation using automatically designed cost functions learned from examples – ground truth = yellow, LOGISMOS = green; White matter/gray matter cost function probability maps learned from cortical segmentation examples in 3D MRI, used for LOGISMOS-B cortical segmentation.

Left-to-right: LOGISMOS programming & development environment, showing MR/CT aortic analysis application; Simultaneous 4D knee MR bone+cartilage segmentation - 8 time-point 3D datasets registered and simultaneously segmented in 4D; Illustration of JEI on knee segmentation; IVUS 3D segmentation facilitated prediction of major adverse cardiac events in coronaries; Segmentation of coronary wall layers in 3D OCT images is a pre-requisite to prediction of cardiac allograft vasculopathy onset in heart-transplant patient; Retinal OCT image analysis with 3D JEI capability in an age-related macular degeneration patient; Brain tumor segmentation.

Videos

LOGISMOS+JEI in action: segment a brain tumor with simple shape, no editing required:

LOGISMOS+JEI in action: segment a brain tumor with complex shape using JEI and merging of objects:

LOGISMOS+JEI in action: segment prostate on MR T2 iamge:

The purpose of these videos is to demonstrate the capability of the algorithm, not to show the clinically correct segmentation.