# Deep LOGISMOS

> **NIH NIH R01** · UNIVERSITY OF IOWA · 2020 · $396,286

## Abstract

Abstract:
This is a competitive continuation of a project that already yielded the highly flexible, accurate, and
broadly applicable LOGISMOS framework for context-aware n-dimensional image segmentation. To
substantially improve and extend its capability, we will develop Deep LOGISMOS that combines and
reinforces the complementary advantages of LOGISMOS and deep learning (DL).
There is growing need for quantitative failure-free 3D and higher-D image analysis for diagnostic and/or
planning purposes. Examples of current use exist in radiation oncology, cardiology, ophthalmology and
other areas of routine clinical medicine, many of which however still rely on manual slice-by-slice tracing.
This manual nature of such analyses hinders their use in precision medicine. Deep LOGISMOS research
proposed here will solve this problem and will offer routine efficient analysis of clinical images of
analyzable quality.
To stimulate a new phase of this research project, we hypothesize that: Advanced graph-based image
segmentation algorithms, when combined with deep-learning-derived application/modality specific
parameters and allowing highly efficient expert-analyst guidance working in concert with the
segmentation algorithms, will significantly increase quantitative analysis performance in routinely
acquired, complex, diagnostic-quality medical images across diverse application areas.
The proposed research focuses on establishing an image segmentation and analysis framework
combining the strengths of LOGISMOS and DL, developing a new way to efficiently generate training
data necessary for learning from examples, forming a failure-free strategy for 3D, 4D, and generally n-D
quantitative medical image analysis, and discovering ways for automated segmentation quality control.
We will fulfill these specific aims:
 1. Develop an efficient approach for building large segmentation training datasets in 3D, 4D, n-D
 using assisted and suggestive annotations.
 2. Develop Deep LOGISMOS, combining two well-established algorithmic strategies – deep learning
 and LOGISMOS graph search.
 3. Develop and validate methods employing deep learning for quality control of Deep LOGISMOS.
 4. In healthcare-relevant applications, demonstrate that Deep LOGISMOS improves segmentation
 performance in comparison with state-of-the-art segmentation techniques.
Deep LOGISMOS will bring broadly available routine quantification of clinical images, positively
impacting the role of reliable image-based information in tomorrow’s precision medicine.

## Key facts

- **NIH application ID:** 10016301
- **Project number:** 5R01EB004640-13
- **Recipient organization:** UNIVERSITY OF IOWA
- **Principal Investigator:** John M Buatti
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $396,286
- **Award type:** 5
- **Project period:** 2006-04-01 → 2023-05-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10016301

## Citation

> US National Institutes of Health, RePORTER application 10016301, Deep LOGISMOS (5R01EB004640-13). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10016301. Licensed CC0.

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