A semiautomatic segmentation method for prostate in <scp>CT</scp> images using local texture classification and statistical shape modeling

Shahedi, Maysam; Halicek, Martin; Guo, Rongrong; Zhang, Guoyi; Schuster, David M.; Fei, Baowei

doi:10.1002/mp.12898

Cited by 21 publications

(14 citation statements)

References 29 publications

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“…The use of a texture analysis applied to imaging studies including CT and MRI have been previously performed for the evaluation of multiple nonneoplastic disorders including the evaluation for mesial temporal sclerosis on MRI, evaluation of intervertebral disc disease on MRI, evaluation of hepatic fibrosis on both CT and MRI, evaluation of subchondral bone on MRI . Prior oncologic studies have also employed texture analyses to evaluate specific tumor features including the assessment of HPV status of oropharyngeal squamous cell carcinomas, prognosis of head and neck neoplasms, classification of gastric and colorectal tumors on CT, genomic mapping and predictive marker identification of gliomas on MRI, the identification of potentially prognostic predictors in lung cancer, evaluation of genitourinary neoplasms on both CT and MRI, and for the radiomic classifications of breast carcinoma subtypes …”

Section: Introductionmentioning

confidence: 99%

Quantitative variations in texture analysis features dependent on MRI scanning parameters: A phantom model

Buch

Kuno

Qureshi

et al. 2018

J Applied Clin Med Phys

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ObjectivesTo evaluate the influence of MRI scanning parameters on texture analysis features.MethodsPublicly available data from the Reference Image Database to Evaluate Therapy Response (RIDER) project sponsored by The Cancer Imaging Archive included MRIs on a phantom comprised of 18 25‐mm doped, gel‐filled tubes, and 1 20‐mm tube containing 0.25 mM Gd‐DTPA (EuroSpinII Test Object5, Diagnostic Sonar, Ltd, West Lothian, Scotland). MRIs performed on a 1.5 T GE HD, 1.5 T Siemens Espree (VB13), or 3.0 T GE HD with TwinSpeed gradients with an eight‐channel head coil included T1WIs with multiple flip angles (flip‐angle = 2,5,10,15,20,25,30), TR/TE = 4.09–5.47/0.90–1.35 ms, NEX = 1 and DCE with 30° flip‐angle, TR/TE=4.09–5.47/0.90–1.35, and NEX = 1,4. DICOM data were imported into an in‐house developed texture analysis program which extracted 41‐texture features including histogram, gray‐level co‐occurrence matrix (GLCM), and gray‐level run‐length (GLRL). Two‐tailed t tests, corrected for multiple comparisons (Q values) were calculated to compare changes in texture features with variations in MRI scanning parameters (magnet strength, flip‐angle, number of excitations (NEX), scanner platform).ResultsSignificant differences were seen in histogram features (mean, median, standard deviation, range) with variations in NEX (Q = 0.003–0.045) and scanner platform (Q < 0.0001), GLCM features (entropy, contrast, energy, and homogeneity) with NEX (Q = 0.001–0.018) and scanner platform (Q < 0.0001), GLRL features (long‐run emphasis, high gray‐level run emphasis, high gray‐level emphasis) with magnet strength (Q = 0.0003), NEX (Q = 0.003–0.022) and scanner platform (Q < 0.0001).ConclusionSignificant differences were seen in many texture features with variations in MRI acquisition emphasizing the need for standardized MRI technique.

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Section: Introductionmentioning

confidence: 99%

Quantitative variations in texture analysis features dependent on MRI scanning parameters: A phantom model

Buch

Kuno

Qureshi

et al. 2018

J Applied Clin Med Phys

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“…These results suggest that the use of 15-20 sparse manually selected surface points achieves a segmentation performance close to manual segmentation. According to our previous studies 13,14 , manual selection of 12 prostate surface points on both MRI and CT images could be done within 20 seconds, which is considerably shorter than the average time of manual prostate MRI segmentation, as reported in the literature 15,16 . Therefore, we think minimal user interaction could be helpful to improve the segmentation accuracy significantly.…”

Section: Testing Resultsmentioning

confidence: 85%

Incorporating minimal user input into deep learning based image segmentation

Shahedi

Halicek

Dormer

et al. 2020

Medical Imaging 2020: Image Processing

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Computer-assisted image segmentation techniques could help clinicians to perform the border delineation task faster with lower inter-observer variability. Recently, convolutional neural networks (CNNs) are widely used for automatic image segmentation. In this study, we used a technique to involve observer inputs for supervising CNNs to improve the accuracy of the segmentation performance. We added a set of sparse surface points as an additional input to supervise the CNNs for more accurate image segmentation. We tested our technique by applying minimal interactions to supervise the networks for segmentation of the prostate on magnetic resonance images. We used U-Net and a new network architecture that was based on U-Net (dualinput path [DIP] U-Net), and showed that our supervising technique could significantly increase the segmentation accuracy of both networks as compared to fully automatic segmentation using U-Net. We also showed DIP U-Net outperformed U-Net for supervised image segmentation. We compared our results to the measured inter-expert observer difference in manual segmentation. This comparison suggests that applying about 15 to 20 selected surface points can achieve a performance comparable to manual segmentation.

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“…GLCM Energy is a measure of orderliness, and is strongest for repeated patterns of similar structures. GLCM texture analysis has been used in a number of radiomics studies to identify disease states by CT, by PET, and by PET/CT . Image texture analysis approaches also have been applied to optimize choices of tomographic reconstruction approaches and parameters …”

Section: Discussionmentioning

confidence: 99%

Texture analysis for automated evaluation of Jaszczak phantom SPECT system tests

2018

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Purpose: Routine quarterly quality assurance (QA) assessment of single photon emission computed tomography (SPECT) systems includes analysis of multipurpose phantoms containing spheres and rods of various sizes. When evaluated by accreditation agencies, criteria applied to assess image quality are largely subjective. Determining a quantified image characteristic metric that emulates human reader impressions of image quality could be quite useful. Our investigation was conducted to ascertain whether image texture analysis metrics, such as those applied to PET scans to detect neoplasms, could prove helpful in linking qualitative statements of phantom sphere and rod visibility to quantified parameters. Because it is not obvious whether it is preferable to submit reconstructions to accrediting agencies performed using typical clinical (CLIN) protocol processing parameters or to follow agencies' filtered backprojection (FBP) suggestions, we applied texture analysis metrics to determine the degree to which these choices affect equipment capability assessment. Methods and materials: Data were processed retrospectively for 125 different Tc-99 m SPECT scans of standardized phantoms for 14 rotating Anger detector systems as part of routine quarterly QA. Algorithms were written to compute several classes of image metrics: quantile curve metrics, image texture analysis gray-level co-occurrence matrix (GLCM) metrics, contrast metrics, and count histogram metrics. For qualitative image scores, two experienced physicists independently graded sphere and rod visibility on a 5-level scale and assigned dichotomous visibility scores, without knowledge of quantified texture analysis metrics or each other's readings. The same phantom was used to collect 15 additional data sets with two dual-detector SPECT/CT systems, reconstructed both by FBP parameters that have been suggested by accrediting agencies and by manufacturers' default settings for CLIN SPECT/CT bone imaging protocols by ordered subsets expectation maximization (OSEM), incorporating attenuation correction using the CT scan. Image characteristics metrics were compared for FBP and CLIN reconstructions. Results: For spheres, the metric with the strongest rank correlation with 5-level scale readings was the quantile curve slope (q = 0.83, P < 0.0001), while for rods it was GLCM Energy normalized to the maximum GLCM Energy value (EnergyNorm) (q = À0.88, P < 0.0001). Compared to dichotomous readings, the metric with the highest ROC area under curve (AUC) for spheres was the quantile curve slopes (AUC = 96 AE 1%, sensitivity = 91%, specificity = 90%), and for rods was Energy-Norm (AUC = 98 AE 1%, sensitivity = 92%, specificity = 95%). Image contrast was higher for all sphere sizes and rod EnergyNorm was lower for sectors of intermediate-sized rods for FBP compared to CLIN reconstructions, in agreement with more rods judged to be visible from FBP than CLIN reconstructions (47% vs 33%, P = 0.002). Conclusions: When preparing to submit quality assurance images of standardized phanto...

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A semiautomatic segmentation method for prostate in CT images using local texture classification and statistical shape modeling

Cited by 21 publications

References 29 publications

Quantitative variations in texture analysis features dependent on MRI scanning parameters: A phantom model

Quantitative variations in texture analysis features dependent on MRI scanning parameters: A phantom model

Incorporating minimal user input into deep learning based image segmentation

Texture analysis for automated evaluation of Jaszczak phantom SPECT system tests

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