Hausdorff Distances for Searching in Binary Text Images

Andreev, A.; Киров, Н.

doi:10.55630/sjc.2009.3.23-46

Cited by 4 publications

(1 citation statement)

References 12 publications

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“…Hausdorff Distance (HD) measures as the distance between two compact nonempty subsets of a metric space [30]. In order to find similarity between predicted (Pred) and ground truth (GT) HD measure between two closed and bounded subsets A and B of a given metric space M is defined as.…”

Section: Performance Analysismentioning

confidence: 99%

MultiRes Attention Deep Learning Approach for Abdominal Fat Compartment Segmentation and Quantification

Prakash¹,

Srinivasa²,

Yeow³

et al. 2023

Deep Learning and Reinforcement Learning

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Global increase in obesity has led to alarming rise in co-morbidities leading to deteriorated quality of life. Obesity phenotyping benefits profiling and management of the condition but warrants accurate quantification of fat compartments. Manual quantification MR scans are time consuming and laborious. Hence, many studies rely on semi/automatic methods for quantification of abdominal fat compartments. We propose a MultiRes-Attention U-Net with hybrid loss function for segmentation of different abdominal fata compartments namely (i) Superficial subcutaneous adipose tissue (SSAT), (ii) Deep subcutaneous adipose tissue (DSAT), and (iii) Visceral adipose tissue (VAT) using abdominal MR scans. MultiRes block, ResAtt-Path, and attention gates can handle shape, scale, and heterogeneity in the data. Dataset involved MR scans from 190 community-dwelling older adults (mainly Chinese, 69.5% females) with mean age—67.85 ± 7.90 years), BMI 23.75 ± 3.65 kg/m2. Twenty-six datasets were manually segmented to generate the ground truth. Data augmentations were performed using MR data acquisition variations. Training and validation were performed on 105 datasets, while testing was conducted on 25 datasets. Median Dice scores were 0.97 for SSAT & DSAT and 0.96 for VAT, and mean Hausdorff distance was <5 mm for all the three fat compartments. Further, MultiRes-Attention U-Net was tested on a new 190 datasets (unseen during training; upper & lower abdomen scans with different resolution), which yielded accurate results. MultiRes-Attention U-Net significantly improved the performance over MultiResUNet, showed excellent generalization and holds promise for body-profiling in large cohort studies.

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Section: Performance Analysismentioning

confidence: 99%

MultiRes Attention Deep Learning Approach for Abdominal Fat Compartment Segmentation and Quantification

Prakash¹,

Srinivasa²,

Yeow³

et al. 2023

Deep Learning and Reinforcement Learning

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Statistical validation of automatic methods for hippocampus segmentation in MR images of epileptic patients

Hosseini

Nazem-Zadeh²,

Pompili

et al. 2014

2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society

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Hippocampus segmentation is a key step in the evaluation of mesial Temporal Lobe Epilepsy (mTLE) by MR images. Several automated segmentation methods have been introduced for medical image segmentation. Because of multiple edges, missing boundaries, and shape changing along its longitudinal axis, manual outlining still remains the benchmark for hippocampus segmentation, which however, is impractical for large datasets due to time constraints. In this study, four automatic methods, namely FreeSurfer, Hammer, Automatic Brain Structure Segmentation (ABSS), and LocalInfo segmentation, are evaluated to find the most accurate and applicable method that resembles the bench-mark of hippocampus. Results from these four methods are compared against those obtained using manual segmentation for T1-weighted images of 157 symptomatic mTLE patients. For performance evaluation of automatic segmentation, Dice coefficient, Hausdorff distance, Precision, and Root Mean Square (RMS) distance are extracted and compared. Among these four automated methods, ABSS generates the most accurate results and the reproducibility is more similar to expert manual outlining by statistical validation. By considering p-value<0.05, the results of performance measurement for ABSS reveal that, Dice is 4%, 13%, and 17% higher, Hausdorff is 23%, 87%, and 70% lower, precision is 5%, -5%, and 12% higher, and RMS is 19%, 62%, and 65% lower compared to LocalInfo, FreeSurfer, and Hammer, respectively.

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Comparative performance evaluation of automated segmentation methods of hippocampus from magnetic resonance images of temporal lobe epilepsy patients

et al. 2016

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The ABSS method for automated hippocampal segmentation outperformed other methods, best approximating what could be achieved by manual tracing. This study also shows that four categories of input data can cause automated segmentation methods to fail. They include incomplete studies, artifact, low signal-to-noise ratio, and inhomogeneity. Different scanner platforms and pulse sequences were considered as means by which to improve reliability of the automated methods. Other modifications were specially devised to enhance a particular method assessed in this study.

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Hausdorff Distances for Searching in Binary Text Images

Cited by 4 publications

References 12 publications

MultiRes Attention Deep Learning Approach for Abdominal Fat Compartment Segmentation and Quantification

MultiRes Attention Deep Learning Approach for Abdominal Fat Compartment Segmentation and Quantification

Statistical validation of automatic methods for hippocampus segmentation in MR images of epileptic patients

Comparative performance evaluation of automated segmentation methods of hippocampus from magnetic resonance images of temporal lobe epilepsy patients

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