Automatic Detection, Localization, and Volume Estimation of Ischemic Infarcts in Noncontrast Computed Tomographic Scans

Nowinski, Wiesław L.; Gupta, Varsha; Qian, Guoyu; He, Jessie; Poh, Lan Eng; Ambrosius, Wojciech; Chrzan, Robert; Polonara, Gabriele; Mazzoni, Cristina; Mól, M.; Salvolini, Luca; Walecki, Jerzy; Salvolini, U.; Urbanik, Andrzej; Kaźmierski, Radosław

doi:10.1097/rli.0b013e31828d8403

Cited by 52 publications

(66 citation statements)

References 20 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Moreover, segmentation algorithms have struggled with variation of slice thickness. [10][11][12]24,25 The segmentation algorithm presented here aimed to combine multiple existent segmentation methodologies in a random forest-based learning approach, which sought to mitigate the methodological limitations mentioned above and improve segmentation results compared with ABC/2.…”

Section: Discussionmentioning

confidence: 99%

Development and Validation of an Automatic Segmentation Algorithm for Quantification of Intracerebral Hemorrhage

Scherer

Cordes

Younsi

et al. 2016

Stroke

View full text Add to dashboard Cite

Background and Purpose-ABC/2 is still widely accepted for volume estimations in spontaneous intracerebral hemorrhage (ICH) despite known limitations, which potentially accounts for controversial outcome-study results. The aim of this study was to establish and validate an automatic segmentation algorithm, allowing for quick and accurate quantification of ICH. Methods-A segmentation algorithm implementing first-and second-order statistics, texture, and threshold features was trained on manual segmentations with a random-forest methodology. Quantitative data of the algorithm, manual segmentations, and ABC/2 were evaluated for agreement in a study sample (n=28) and validated in an independent sample not used for algorithm training (n=30). Results-ABC/2 volumes were significantly larger compared with either manual or algorithm values, whereas no significant differences were found between the latter (P<0.0001; Friedman+Dunn's multiple comparison

show abstract

Section: Discussionmentioning

confidence: 99%

Development and Validation of an Automatic Segmentation Algorithm for Quantification of Intracerebral Hemorrhage

Scherer

Cordes

Younsi

et al. 2016

Stroke

View full text Add to dashboard Cite

show abstract

“…Infarct localization and its volume estimation can be determined automatically [30], which will expedite the procedure.…”

Section: Discussionmentioning

confidence: 99%

“…Moreover, the cases with a midline shift, leukoaraiosis and old infarcts as well as hemorrhages and edemas causing anatomical distortion were excluded. The strict process resulted in selecting for this study a dataset of 128 cases of neurologically confirmed ischemic strokes with all the infarcts delineated (contoured) earlier (as part of another study [30]). The numbers of cases for mRS and BI scores at different days are given in Table 1.…”

Section: Methodsmentioning

confidence: 99%

Population-Based Stroke Atlas for Outcome Prediction: Method and Preliminary Results for Ischemic Stroke from CT

et al. 2014

Self Cite

View full text Add to dashboard Cite

Background and PurposeKnowledge of outcome prediction is important in stroke management. We propose a lesion size and location-driven method for stroke outcome prediction using a Population-based Stroke Atlas (PSA) linking neurological parameters with neuroimaging in population. The PSA aggregates data from previously treated patients and applies them to currently treated patients. The PSA parameter distribution in the infarct region of a treated patient enables prediction. We introduce a method for PSA calculation, quantify its performance, and use it to illustrate ischemic stroke outcome prediction of modified Rankin Scale (mRS) and Barthel Index (BI).MethodsThe preliminary PSA was constructed from 128 ischemic stroke cases calculated for 8 variants (various data aggregation schemes) and 3 case selection variables (infarct volume, NIHSS at admission, and NIHSS at day 7), each in 4 ranges. Outcome prediction for 9 parameters (mRS at 7th, and mRS and BI at 30th, 90th, 180th, 360th day) was studied using a leave-one-out approach, requiring 589,824 PSA maps to be analyzed.ResultsOutcomes predicted for different PSA variants are statistically equivalent, so the simplest and most efficient variant aiming at parameter averaging is employed. This variant allows the PSA to be pre-calculated before prediction. The PSA constrained by infarct volume and NIHSS reduces the average prediction error (absolute difference between the predicted and actual values) by a fraction of 0.796; the use of 3 patient-specific variables further lowers it by 0.538. The PSA-based prediction error for mild and severe outcomes (mRS = [2]–[5]) is (0.5–0.7). Prediction takes about 8 seconds.ConclusionsPSA-based prediction of individual and group mRS and BI scores over time is feasible, fast and simple, but its clinical usefulness requires further studies. The case selection operation improves PSA predictability. A multiplicity of PSAs can be computed independently for different datasets at various centers and easily merged, which enables building powerful PSAs over the community.

show abstract

“…For clinical applications, it is crucial to detect hyperacute changes in NCCT when infarcted tissue cannot be distinguished by the clinical read by an experienced neuroradiologist or neurologist. Hence, the objectives of our study were to: (1) extend the previous SIM algorithm by comparing the detection of early nonvisible infarct in a single NCCT scan, to the gold standard of a fully visible infarcted parenchyma in the subsequent follow‐up examination; (2) analyze the performance of the SIM and its components in the detection of hyperacute ischemic stroke; and (3) improve early infarct detection, by modifying SIM and its components.…”

Section: Introductionmentioning

confidence: 99%

A Quantitative Method Using Head Noncontrast CT Scans to Detect Hyperacute Nonvisible Ischemic Changes in Patients With Stroke

Gomolka

Chrzan

Urbanik

et al. 2016

Journal of Neuroimaging

Self Cite

View full text Add to dashboard Cite

show abstract

Automatic Detection, Localization, and Volume Estimation of Ischemic Infarcts in Noncontrast Computed Tomographic Scans

Cited by 52 publications

References 20 publications

Development and Validation of an Automatic Segmentation Algorithm for Quantification of Intracerebral Hemorrhage

Development and Validation of an Automatic Segmentation Algorithm for Quantification of Intracerebral Hemorrhage

Population-Based Stroke Atlas for Outcome Prediction: Method and Preliminary Results for Ischemic Stroke from CT

A Quantitative Method Using Head Noncontrast CT Scans to Detect Hyperacute Nonvisible Ischemic Changes in Patients With Stroke

Contact Info

Product

Resources

About