# Addressing algorithmic and data challenges to deep learning based segmentation of spine anatomy

> **NIH NIH R01** · UNIVERSITY OF CALIFORNIA LOS ANGELES · 2022 · $77,870

## Abstract

Project Summary/Abstract
In spine medicine, subjective interpretation of biomedical images often leads to wrong diagnoses, prolonged
non-surgical treatment for surgically treatable patients, and surgical treatment when none is necessary. Objective
computerized analysis of the aforementioned images using deep learning has the potential to improve surgical
outcomes while driving down the cost of surgery by eliminating unnecessary surgery and expediting necessary
ones. Yet several barriers stymie the development and deployment of deep learning technology to operationalize
imaging biomarker-based treatment recommendation in surgical practice. First, a publicly available database is
absent to help train and validate algorithms for spinal pathologies. Second deep learning techniques remain
difficult to train and operationalize in the clinical setting, due to various challenges. These include – 1. The lack
of a framework to link generalization error to training data in deep learning-based segmentation, due to which
performance estimates of algorithms are untenable prior to deployment 2. the lack of a disciplined approach to
improve deep network performance on medical image segmentation and 3. the lack of frameworks that enable
deep networks to identify and flag a difficult case and failed cases where a human expert should be consulted.
First, we propose to develop a publicly accessible spine imaging database to promote the development of deep
learning algorithms. Second, we aim to address the aforementioned technical challenges by 1. Developing a
power-law scaling based framework to link training sample size and generalization error analytically 2. Proposing
and validating a mathematical framework to create deep learning ensembles from deep learning models to
guarantee improvement in segmentation performance 3. Developing and validating a Von-Neumann information-
based score to endow deep learning ensembles with the ability to identify difficult cases and predict failure.

## Key facts

- **NIH application ID:** 10367207
- **Project number:** 1R01AR078282-01A1
- **Recipient organization:** UNIVERSITY OF CALIFORNIA LOS ANGELES
- **Principal Investigator:** Bilwaj Gaonkar
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $77,870
- **Award type:** 1
- **Project period:** 2022-08-01 → 2022-12-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10367207

## Citation

> US National Institutes of Health, RePORTER application 10367207, Addressing algorithmic and data challenges to deep learning based segmentation of spine anatomy (1R01AR078282-01A1). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10367207. Licensed CC0.

---

*[NIH grants dataset](/datasets/nih-grants) · CC0 1.0*