Automatic Breast Lesion Segmentation Using Continuous Max-Flow Algorithm in Phase Preserved DCE-MRIs

Pandey, Dinesh; Wang, Hua; Yin, Xiaoxia; Wang, Kate; Zhang, Yanchun; Shen, Jing

doi:10.1007/978-3-030-90885-0_12

Cited by 2 publications

(2 citation statements)

References 32 publications

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“…BNN was sensitive to different NLs if all the NLs were ID of the training data (figure 4). Such sensitivity to variations in NLs is important when dealing with patient data because the NLs in DCE-MR varies due to variation in patient size, administered contrast agent dose, and residual motion and under-sampling artefacts (Jiao et al 2020, Ippoliti et al 2021, Pandey et al 2021. Therefore, the BNN could be applied to different NLs and it could capture the NLs in the data.…”

Section: Discussionmentioning

confidence: 99%

Unified Bayesian network for uncertainty quantification of physiological parameters in dynamic contrast enhanced (DCE) MRI of the liver

Dejene,

Brenner,

Makowski

et al. 2023

Phys. Med. Biol.

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Objective. Physiological parameter estimation is affected by intrinsic ambiguity in the data such as noise and model inaccuracies. The aim of this work is to provide a deep learning framework for accurate parameter and uncertainty estimates for DCE-MRI in the liver. Approach. Concentration time curves are simulated to train a Bayesian neural network (BNN). Training of the BNN involves minimization of a loss function that jointly minimizes the aleatoric and epistemic uncertainties. Uncertainty estimation is evaluated for different noise levels and for different out of distribution (OD) cases, i.e., where the data during inference differs strongly to the data during training. The accuracy of parameter estimates are compared to a non-linear least squares (NLLS) fitting in numerical simulations and in-vivo data of a patient suffering from hepatic tumor lesions. Main results. BNN achieved lower root-mean-squared errors (RMSE) than the NLLS for the simulated data. RMSE of BNN was on overage of all noise levels lower by 33% ± 1.9% for ktrans, 22% ± 6% for ve and 89% ± 5% for vp than the NLLS. The aleatoric uncertainties of the parameters increased with increasing noise level, whereas the epistemic uncertainty increased when a BNN was evaluated with OD data. For the in-vivo data, more robust parameter estimates were obtained by the BNN than the NLLS fit. In addition, the differences between estimated parameters for healthy and tumor regions-of-interest were significant (p< 0.0001). Significance. The proposed framework allowed for accurate parameter estimates for quantitative DCE-MRI. In addition, the BNN provided uncertainty estimates which highlighted cases of high noise and in which the training data did not match the data during inference. This is important for clinical application because it would indicate cases in which the trained model is inadequate and additional training with an adapted training data set is required.

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Section: Discussionmentioning

confidence: 99%

Unified Bayesian network for uncertainty quantification of physiological parameters in dynamic contrast enhanced (DCE) MRI of the liver

Dejene,

Brenner,

Makowski

et al. 2023

Phys. Med. Biol.

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“…Furthermore, breast cancer is said to be the world’s most frequent and fastest-growing cancer, affecting primarily women and caused by aberrant cell development around the breast lobules or ducts [ 5 ]. It is the second most frequent cancer in women that leads to mortality, after lung cancer [ 6 ]. The importance of early identification and therapy in improving the survival rate cannot be overstated.…”

Section: Introductionmentioning

confidence: 99%

Automatic breast lesion segmentation in phase preserved DCE-MRIs

Pandey

Wang

Yin

et al. 2022

Health Inf Sci Syst

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We offer a framework for automatically and accurately segmenting breast lesions from Dynamic Contrast Enhanced (DCE) MRI in this paper. The framework is built using max flow and min cut problems in the continuous domain over phase preserved denoised images. Three stages are required to complete the proposed approach. First, post-contrast and pre-contrast images are subtracted, followed by image registrations that benefit to enhancing lesion areas. Second, a phase preserved denoising and pixel-wise adaptive Wiener filtering technique is used, followed by max flow and min cut problems in a continuous domain. A denoising mechanism clears the noise in the images by preserving useful and detailed features such as edges. Then, lesion detection is performed using continuous max flow. Finally, a morphological operation is used as a post-processing step to further delineate the obtained results. A series of qualitative and quantitative trials employing nine performance metrics on 21 cases with two different MR image resolutions were used to verify the effectiveness of the proposed method. Performance results demonstrate the quality of segmentation obtained from the proposed method.

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Automatic Breast Lesion Segmentation Using Continuous Max-Flow Algorithm in Phase Preserved DCE-MRIs

Cited by 2 publications

References 32 publications

Unified Bayesian network for uncertainty quantification of physiological parameters in dynamic contrast enhanced (DCE) MRI of the liver

Unified Bayesian network for uncertainty quantification of physiological parameters in dynamic contrast enhanced (DCE) MRI of the liver

Automatic breast lesion segmentation in phase preserved DCE-MRIs

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