Automatic segmentation for detecting uterine fibroid regions treated with MR-guided high intensity focused ultrasound (MR-HIFU)

Antila, Kari; Nieminen, Heikki J.; Sequeiros, Roberto Blanco; Ehnholm, Gösta

doi:10.1118/1.4881319

Cited by 7 publications

(8 citation statements)

References 16 publications

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“…28,29 Enhanced MRI was considered an important evaluation measure for the early therapeutic response of uterine fibroids to HIFU. 18,30,31 The extent of the nonperfused volume or nonperfused volume ratio obtained from posttreatment enhanced MRI was significantly correlated with the treatment outcome for uterine fibroids. 31 An increase in the nonperfused volume ratio was associated with an increase in the fibroid volume reduction ratio and symptomatic relief.…”

Section: Discussionmentioning

confidence: 97%

“…It has been revealed that HIFU has a therapeutic effect on uterine fibroids mainly by causing coagulation necrosis of targeted tissue, which was presented as a nonenhancement characteristic on enhanced MRI . Enhanced MRI was considered an important evaluation measure for the early therapeutic response of uterine fibroids to HIFU . The extent of the nonperfused volume or nonperfused volume ratio obtained from posttreatment enhanced MRI was significantly correlated with the treatment outcome for uterine fibroids .…”

Section: Discussionmentioning

confidence: 99%

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Predictive Value of Quantitative Uterine Fibroid Perfusion Parameters From Contrast‐Enhanced Ultrasound for the Therapeutic Effect of High‐Intensity Focused Ultrasound Ablation

Wang

Zhang

et al. 2018

J of Ultrasound Medicine

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Objectives To assess the predictive significance of quantitative perfusion parameters from contrast‐enhanced ultrasound (CEUS) for the therapeutic response to high‐intensity focused ultrasound (HIFU) ablation in patients with uterine fibroids. Methods A total of 263 patients with single uterine fibroids were treated with HIFU ablation under ultrasound guidance. The arrival time, peak time, enhancement time, enhancement intensity, and enhancement rate were evaluated with pretreatment CEUS. According to a nonperfused volume ratio evaluation by posttreatment magnetic resonance imaging, all patients were assigned to groups with volume ratios of 70% or higher and lower than 70%. Then the predictive performances of different parameters for ablation efficacy were studied. Results The arrival time, peak time, and enhancement time in the group with a nonperfused volume ratio of 70% or higher were longer than those in the group with a volume ratio lower than 70% (mean ± SD, 16.7 ± 3.5, 26.5 ± 4.9, and 10.2 ± 2.6 seconds, respectively, versus 13.3 ± 4.2, 20.8 ± 5.4, and 7.6 ± 2.3 seconds), whereas patients with a volume ratio of 70% or higher had a lower mean enhancement intensity and enhancement rate than those with a volume ratio lower than 70% (29.7 ± 16.7 dB and 3.2 ± 1.5 dB/s versus 63.2 ± 26.3 dB and 8.6 ± 4.3 dB/s; P < .05). The nonperfused volume ratio was negatively correlated with the enhancement intensity and enhancement rate (r = –0.631 and –0.712) but positively correlated with the arrival time, peak time, and enhancement time (r = 0.322, 0.456, and 0.477; P < .05). The areas under the receiver operating characteristic curve for the enhancement time, enhancement intensity, and enhancement rate were 0.73, 0.79, and 0.81 (P < .05). Conclusions Quantitative parameters from CEUS are potentially useful for evaluating the therapeutic effect of HIFU ablation for uterine fibroids.

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Section: Discussionmentioning

confidence: 97%

Section: Discussionmentioning

confidence: 99%

Predictive Value of Quantitative Uterine Fibroid Perfusion Parameters From Contrast‐Enhanced Ultrasound for the Therapeutic Effect of High‐Intensity Focused Ultrasound Ablation

Wang

Zhang

et al. 2018

J of Ultrasound Medicine

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“…Other mixed methods: Antila et al [38] designed an automatic segmentation pipeline without user input. They applied the active shape model (ASM) to get the deformed surface, and classified PV (perfused volume: the untreated tissue) and NPV (non-perfused volume: the treated tissue) by an expectation maximization (EM) algorithm.…”

Section: A Conventional Methods Of Uterus and Uterine Fibroid Segmenmentioning

confidence: 99%

HIFUNet: Multi-Class Segmentation of Uterine Regions From MR Images Using Global Convolutional Networks for HIFU Surgery Planning

Zhang

Yang

et al. 2020

IEEE Trans. Med. Imaging

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Accurate segmentation of uterus, uterine fibroids, and spine from MR images is crucial for high intensity focused ultrasound (HIFU) therapy but remains still difficult to achieve because of 1) the large shape and size variations among individuals, 2) the low contrast between adjacent organs and tissues, and 3) the unknown number of uterine fibroids. To tackle this problem, in this paper, we propose a large kernel Encoder-Decoder Network based on a 2D segmentation model. The use of this large kernel can capture multi-scale contexts by enlarging the valid receptive field. In addition, a deep multiple atrous convolution block is also employed to enlarge the receptive field and extract denser feature maps. Our approach is compared to both conventional and other deep learning methods and the experimental results conducted on a large dataset show its effectiveness.

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“…Image registration steps are mandatory and are performed using the external software tool Medical Image Processing, Analysis, and Visualization (MIPAV) [397]. In [22], an automatic method for treated fibroid segmentation is proposed. Both the post-operative CE-MR image stack (interpolated to a 3D volume) and the treatment plan (i.e., file that stores the sonication cells used during the MRgFUS treatment) are required.…”

Section: Mrgfus Treatments For Uterine Fibroidsmentioning

confidence: 99%

MedGA: A novel evolutionary method for image enhancement in medical imaging systems

Rundo

Tangherloni

Nobile

et al. 2019

Expert Systems with Applications

102

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Medical imaging systems often require the application of image enhancement techniques to help physicians in anomaly/abnormality detection and diagnosis, as well as to improve the quality of images that undergo automated image processing. In this work we introduce MedGA, a novel image enhancement method based on Genetic Algorithms that is able to improve the appearance and the visual quality of images characterized by a bimodal gray level intensity histogram, by strengthening their two underlying sub-distributions. MedGA can be exploited as a pre-processing step for the enhancement of images with a nearly bimodal histogram distribution, to improve the results achieved by downstream image processing techniques. As a case study, we use MedGA as a clinical expert system for contrast-enhanced Magnetic Resonance image analysis, considering Magnetic Resonance guided Focused Ultrasound Surgery for uterine fibroids. The performances of MedGA are quantitatively evaluated by means of various image enhancement metrics, and compared against the conventional state-of-the-art image enhancement techniques, namely, histogram

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Automatic segmentation for detecting uterine fibroid regions treated with MR-guided high intensity focused ultrasound (MR-HIFU)

Abstract: The results suggest that the segmentation algorithm developed, requiring no user-input, provides a feasible and practical approach for the automatic evaluation of the boundary and volume of the HIFU-treated region.

Cited by 7 publications

References 16 publications

Predictive Value of Quantitative Uterine Fibroid Perfusion Parameters From Contrast‐Enhanced Ultrasound for the Therapeutic Effect of High‐Intensity Focused Ultrasound Ablation

Predictive Value of Quantitative Uterine Fibroid Perfusion Parameters From Contrast‐Enhanced Ultrasound for the Therapeutic Effect of High‐Intensity Focused Ultrasound Ablation

HIFUNet: Multi-Class Segmentation of Uterine Regions From MR Images Using Global Convolutional Networks for HIFU Surgery Planning

MedGA: A novel evolutionary method for image enhancement in medical imaging systems

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