Comparison between two packages for pectoral muscle removal on mammographic images

Sansone, Mario; Marrone, Stefano; Salvio, Giusi Di; Belfiore, Maria Paola; Gatta, Gianluca; Fusco, Roberta; Vanore, Laura; Zuiani, Chiara; Grassi, Francesca; Vietri, Maria Teresa; Granata, Vincenza; Grassi, Roberto

doi:10.1007/s11547-022-01521-5

Cited by 18 publications

(11 citation statements)

References 38 publications

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“…The results of our study are in line with previous studies [ 3 , 4 , 13 , 15 , 23 , 25 , 26 , 30 , 39 , 40 , 41 , 42 , 43 , 44 , 45 , 46 , 47 ]; however, as a difference, we specifically addressed segmentation variability with a fixed CT device but analysed the dependence on bin-width, distance, resolution or interpolator.…”

Section: Discussionsupporting

confidence: 88%

Robustness of Radiomics in Pre-Surgical Computer Tomography of Non-Small-Cell Lung Cancer

Belfiore

Sansone

Monti

et al. 2022

JPM

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Background: Radiomic features are increasingly used in CT of NSCLC. However, their robustness with respect to segmentation variability has not yet been demonstrated. The aim of this study was to assess radiomic features agreement across three kinds of segmentation. Methods: We retrospectively included 48 patients suffering from NSCLC who underwent pre-surgery CT. Two expert radiologists in consensus manually delineated three 3D-ROIs on each patient. To assess robustness for each feature, the intra-class correlation coefficient (ICC) across segmentations was evaluated. The ‘sensitivity’ of ICC upon some parameters affecting features computation (such as bin-width for first-order features and pixel-distances for second-order features) was also evaluated. Moreover, an assessment with respect to interpolator and isotropic resolution was also performed. Results: Our results indicate that ‘shape’ features tend to have excellent agreement (ICC > 0.9) across segmentations; moreover, they have approximately zero sensitivity to other parameters. ‘First-order’ features are in general sensitive to parameters variation; however, a few of them showed excellent agreement and low sensitivity (below 0.1) with respect to bin-width and pixel-distance. Similarly, a few second-order features showed excellent agreement and low sensitivity. Conclusions: Our results suggest that a limited number of radiomic features can achieve a high level of reproducibility in CT of NSCLC.

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Section: Discussionsupporting

confidence: 88%

Robustness of Radiomics in Pre-Surgical Computer Tomography of Non-Small-Cell Lung Cancer

Belfiore

Sansone

Monti

et al. 2022

JPM

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“…We estimate the percentage of total pixels that make up the FP and FN with respect to the true pectoral muscle regions on each mammogram. Because prior findings identified Libra to be superior in terms of accuracy when compared to OpenBreast [ 2 ], we compare our proposed algorithm with Libra in the subsequent section. We present FP and FN for both the proposed method and for application of Libra to the same set of 1,902 study images.…”

Section: Methodsmentioning

confidence: 99%

“…In a recent study [ 2 ], comparison was made between two commonly used methods, namely Libra [ 3 ] and OpenBreast [ 4 ] for pectoral muscle removal in full-field digital mammogram (FFDM) images. That study included 168 women revealing that Libra exhibited superior performance in terms of accuracy when compared to OpenBreast.…”

Section: Introductionmentioning

confidence: 99%

Pectoral muscle removal in mammogram images: A novel approach for improved accuracy and efficiency

Chen,

Bennett,

Colditz

et al. 2023

Cancer Causes Control

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Purpose Accurate pectoral muscle removal is critical in mammographic breast density estimation and many other computer-aided algorithms. We propose a novel approach to remove pectoral muscles form mediolateral oblique (MLO) view mammograms and compare accuracy and computational efficiency with existing method (Libra). Methods A pectoral muscle identification pipeline was developed. The image is first binarized to enhance contrast and then the Canny algorithm was applied for edge detection. Robust interpolation is used to smooth out the pectoral muscle region. Accuracy and computational speed of pectoral muscle identification was assessed using 951 women (1,902 MLO mammograms) from the Joanne Knight Breast Health Cohort at Washington University School of Medicine. Results Our proposed algorithm exhibits lower mean error of 12.22% in comparison to Libra’s estimated error of 20.44%. This 40% gain in accuracy was statistically significant (p < 0.001). The computational time for the proposed algorithm is 5.4 times faster when compared to Libra (5.1 s for proposed vs. 27.7 s for Libra per mammogram). Conclusion We present a novel approach for pectoral muscle removal in mammogram images that demonstrates significant improvement in accuracy and efficiency compared to existing method. Our findings have important implications for the development of computer-aided systems and other automated tools in this field.

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“…The current PC screening programs are based on the annual employment of magnetic resonance imaging with cholangiopancreatography sequences (MR/MRCP) and/or endoscopic ultrasonography (EUS) [138,[141][142][143][144][145][146][147][148][149][150][151][152]. For patients unfit for MRI, computed tomography (CT) could be proposed, although CT shows lower detection rates, compared to MRI, for small lesions [153][154][155][156][157][158][159][160][161][162][163].…”

Section: Screening Modalitiesmentioning

confidence: 99%

Risk Assessment and Pancreatic Cancer: Diagnostic Management and Artificial Intelligence

Granata

Fusco

Setola

et al. 2023

Cancers

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Pancreatic cancer (PC) is one of the deadliest cancers, and it is responsible for a number of deaths almost equal to its incidence. The high mortality rate is correlated with several explanations; the main one is the late disease stage at which the majority of patients are diagnosed. Since surgical resection has been recognised as the only curative treatment, a PC diagnosis at the initial stage is believed the main tool to improve survival. Therefore, patient stratification according to familial and genetic risk and the creation of screening protocol by using minimally invasive diagnostic tools would be appropriate. Pancreatic cystic neoplasms (PCNs) are subsets of lesions which deserve special management to avoid overtreatment. The current PC screening programs are based on the annual employment of magnetic resonance imaging with cholangiopancreatography sequences (MR/MRCP) and/or endoscopic ultrasonography (EUS). For patients unfit for MRI, computed tomography (CT) could be proposed, although CT results in lower detection rates, compared to MRI, for small lesions. The actual major limit is the incapacity to detect and characterize the pancreatic intraepithelial neoplasia (PanIN) by EUS and MR/MRCP. The possibility of utilizing artificial intelligence models to evaluate higher-risk patients could favour the diagnosis of these entities, although more data are needed to support the real utility of these applications in the field of screening. For these motives, it would be appropriate to realize screening programs in research settings.

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Comparison between two packages for pectoral muscle removal on mammographic images

Cited by 18 publications

References 38 publications

Robustness of Radiomics in Pre-Surgical Computer Tomography of Non-Small-Cell Lung Cancer

Robustness of Radiomics in Pre-Surgical Computer Tomography of Non-Small-Cell Lung Cancer

Pectoral muscle removal in mammogram images: A novel approach for improved accuracy and efficiency

Risk Assessment and Pancreatic Cancer: Diagnostic Management and Artificial Intelligence

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