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Minimizing manual image segmentation turn-around time for neuronal reconstruction by embracing uncertainty.

ABSTRACT: The ability to automatically segment an image into distinct regions is a critical aspect in many visual processing applications. Because inaccuracies often exist in automatic segmentation, manual segmentation is necessary in some application domains to correct mistakes, such as required in the reconstruction of neuronal processes from microscopic images. The goal of the automated segmentation tool is traditionally to produce the highest-quality segmentation, where quality is measured by the similarity to actual ground truth, so as to minimize the volume of manual correction necessary. Manual correction is generally orders-of-magnitude more time consuming than automated segmentation, often making handling large images intractable. Therefore, we propose a more relevant goal: minimizing the turn-around time of automated/manual segmentation while attaining a level of similarity with ground truth. It is not always necessary to inspect every aspect of an image to generate a useful segmentation. As such, we propose a strategy to guide manual segmentation to the most uncertain parts of segmentation. Our contributions include 1) a probabilistic measure that evaluates segmentation without ground truth and 2) a methodology that leverages these probabilistic measures to significantly reduce manual correction while maintaining segmentation quality.

SUBMITTER: Plaza SM

PROVIDER: S-EPMC3448604 | biostudies-literature | 2012

REPOSITORIES: biostudies-literature

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Minimizing manual image segmentation turn-around time for neuronal reconstruction by embracing uncertainty.

Plaza Stephen M SM Scheffer Louis K LK Saunders Mathew M

PloS one 20120921 9

The ability to automatically segment an image into distinct regions is a critical aspect in many visual processing applications. Because inaccuracies often exist in automatic segmentation, manual segmentation is necessary in some application domains to correct mistakes, such as required in the reconstruction of neuronal processes from microscopic images. The goal of the automated segmentation tool is traditionally to produce the highest-quality segmentation, where quality is measured by the simi ...[more]

PMID: 23028540

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Project description:Recent advances in multi-atlas based algorithms address many of the previous limitations in model-based and probabilistic segmentation methods. However, at the label fusion stage, a majority of algorithms focus primarily on optimizing weight-maps associated with the atlas library based on a theoretical objective function that approximates the segmentation error. In contrast, we propose a novel method-Autocorrecting Walks over Localized Markov Random Fields (AWoL-MRF)-that aims at mimicking the sequential process of manual segmentation, which is the gold-standard for virtually all the segmentation methods. AWoL-MRF begins with a set of candidate labels generated by a multi-atlas segmentation pipeline as an initial label distribution and refines low confidence regions based on a localized Markov random field (L-MRF) model using a novel sequential inference process (walks). We show that AWoL-MRF produces state-of-the-art results with superior accuracy and robustness with a small atlas library compared to existing methods. We validate the proposed approach by performing hippocampal segmentations on three independent datasets: (1) Alzheimer's Disease Neuroimaging Database (ADNI); (2) First Episode Psychosis patient cohort; and (3) A cohort of preterm neonates scanned early in life and at term-equivalent age. We assess the improvement in the performance qualitatively as well as quantitatively by comparing AWoL-MRF with majority vote, STAPLE, and Joint Label Fusion methods. AWoL-MRF reaches a maximum accuracy of 0.881 (dataset 1), 0.897 (dataset 2), and 0.807 (dataset 3) based on Dice similarity coefficient metric, offering significant performance improvements with a smaller atlas library (< 10) over compared methods. We also evaluate the diagnostic utility of AWoL-MRF by analyzing the volume differences per disease category in the ADNI1: Complete Screening dataset. We have made the source code for AWoL-MRF public at: https://github.com/CobraLab/AWoL-MRF.

Project description:Evaluating the quality of reconstructed images requires consistent approaches to extracting information and applying metrics. Partitioning medical images into tissue types permits the quantitative assessment of regions that contain a specific tissue. The assessment facilitates the evaluation of an imaging algorithm in terms of its ability to reconstruct the properties of various tissue types and identify anomalies. Microwave tomography is an imaging modality that is model-based and reconstructs an approximation of the actual internal spatial distribution of the dielectric properties of a breast over a reconstruction model consisting of discrete elements. The breast tissue types are characterized by their dielectric properties, so the complex permittivity profile that is reconstructed may be used to distinguish different tissue types. This manuscript presents a robust and flexible medical image segmentation technique to partition microwave breast images into tissue types in order to facilitate the evaluation of image quality. The approach combines an unsupervised machine learning method with statistical techniques. The key advantage for using the algorithm over other approaches, such as a threshold-based segmentation method, is that it supports this quantitative analysis without prior assumptions such as knowledge of the expected dielectric property values that characterize each tissue type. Moreover, it can be used for scenarios where there is a scarcity of data available for supervised learning. Microwave images are formed by solving an inverse scattering problem that is severely ill-posed, which has a significant impact on image quality. A number of strategies have been developed to alleviate the ill-posedness of the inverse scattering problem. The degree of success of each strategy varies, leading to reconstructions that have a wide range of image quality. A requirement for the segmentation technique is the ability to partition tissue types over a range of image qualities, which is demonstrated in the first part of the paper. The segmentation of images into regions of interest corresponding to various tissue types leads to the decomposition of the breast interior into disjoint tissue masks. An array of region and distance-based metrics are applied to compare masks extracted from reconstructed images and ground truth models. The quantitative results reveal the accuracy with which the geometric and dielectric properties are reconstructed. The incorporation of the segmentation that results in a framework that effectively furnishes the quantitative assessment of regions that contain a specific tissue is also demonstrated. The algorithm is applied to reconstructed microwave images derived from breasts with various densities and tissue distributions to demonstrate the flexibility of the algorithm and that it is not data-specific. The potential for using the algorithm to assist in diagnosis is exhibited with a tumor tracking example. This example also establishes the usefulness of the approach in evaluating the performance of the reconstruction algorithm in terms of its sensitivity and specificity to malignant tissue and its ability to accurately reconstruct malignant tissue.

Project description:BackgroundTrainees in acute care specialties often grapple with the decision to perform an invasive procedure in a rapidly decompensating patient, for whom the benefits and risks are inherently uncertain. The difference between trainees who know when to act and when to seek supervision and those who do not is often linked to individual trainee psychological and cultural perceptions of uncertainty. But how much comfort with uncertainty relates to the situational context rather than the trainee traits is underexplored.ObjectiveThe objective of this study was to explore trainee actions around decompensating patients and assess the degree to which invasive intervention and supervision seeking depend on situational certainty or individual trait-based perceptions of uncertainty.MethodsA total of 41 internal medicine residents completed a survey to measure anxiety related to uncertainty using the Physicians' Reactions to Uncertainty (PRU) tool and to measure uncertainty avoidance using the Values Survey Module (VSM) before responding to 14 written emergency situations. Half of the scenarios contain sufficient diagnostic certainty to warrant aggressive intervention, and half lack sufficient diagnostic clarity to offset the risk of intervention. Mixed multivariable modeling was used to identify the relationship between planned invasive intervention, situational uncertainty, and trait-based perceptions of uncertainty measured in the PRU and VSM.ResultsTrainees' first actions were appropriate in 60% of cases. Multivariable modeling suggested that situational certainty was more predictive of upfront intervention (odds ratio [OR], 30.5; P < 0.0001) than trait-based PRU (OR, 1.22; P = 0.05) and VSM (OR, 1.73; P < 0.0001). Similarly, situational certainty was more predictive of reduced supervision seeking (OR, 0.20; P < 0.0001) than trait-based PRU (OR, 2.03; P < 0.001) and VSM (P = not significant).ConclusionsSituation-specific certainty was more strongly correlated with invasive intervention in cases of decompensated patients than individual trainee traits. Focusing on trainee contextual understanding of procedural risk-benefit ratios in decompensating patients holds more promise for improving trainee actions and supervision seeking than tackling their perceptions around uncertainty.

Dataset Information

Minimizing manual image segmentation turn-around time for neuronal reconstruction by embracing uncertainty.

Publications

Minimizing manual image segmentation turn-around time for neuronal reconstruction by embracing uncertainty.

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