Using deep learning to segment breast and fibroglandular tissue in MRI volumes

Dalmış, Mehmet Ufuk; Litjens, Geert; Holland, Katharina; Setio, Arnaud Arindra Adiyoso; Mann, Ritse M.; Karssemeijer, Nico; Gubern-Mérida, Albert

doi:10.1002/mp.12079

Cited by 193 publications

(137 citation statements)

References 29 publications

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“…Our results confirm the observation made by others that the U‐Net architecture is able to produce good results even when the number of training samples is small if it is used in a restricted domain of images.…”

Section: Discussionsupporting

confidence: 90%

“…It is, however, still important to carry out this step, as there was a significant difference between the best and worst performing parameter sets in all of the studies. Dalmis et al . used the original U‐net architecture described by Ronneberger et al .…”

Section: Discussionmentioning

confidence: 99%

“…A survey by Litjens et al., carried out in early 2017, reviews the use of deep learning for medical image analysis and lists 350 papers related to this topic. Dalmiş et al . used a two‐dimensional (2D) U‐Net architecture to segment breast MRI into three classes: breast, background, and fibroglandular tissue, and in another study, deep learning are used to segment the pectoral muscle in 2D MR images .…”

Section: Background and Introductionmentioning

confidence: 99%

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An investigation of the effect of fat suppression and dimensionality on the accuracy of breast MRI segmentation using U‐nets

Fashandi

Kuling

et al. 2019

Medical Physics

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Purpose Accurate segmentation of the breast is required for breast density estimation and the assessment of background parenchymal enhancement, both of which have been shown to be related to breast cancer risk. The MRI breast segmentation task is challenging, and recent work has demonstrated that convolutional neural networks perform well for this task. In this study, we have investigated the performance of several two‐dimensional (2D) U‐Net and three‐dimensional (3D) U‐Net configurations using both fat‐suppressed and nonfat‐suppressed images. We have also assessed the effect of changing the number and quality of the ground truth segmentations. Materials and methods We designed eight studies to investigate the effect of input types and the dimensionality of the U‐Net operations for the breast MRI segmentation. Our training data contained 70 whole breast volumes of T1‐weighted sequences without fat suppression (WOFS) and with fat suppression (FS). For each subject, we registered the WOFS and FS volumes together before manually segmenting the breast to generate ground truth. We compared four different input types to the U‐nets: WOFS, FS, MIXED (WOFS and FS images treated as separate samples), and MULTI (WOFS and FS images combined into a single multichannel image). We trained 2D U‐Nets and 3D U‐Nets with these data, which resulted in our eight studies (2D‐WOFS, 3D‐WOFS, 2D‐FS, 3D‐FS, 2D‐MIXED, 3D‐MIXED, 2D‐MULTI, and 3D‐MULT). For each of these studies, we performed a systematic grid search to tune the hyperparameters of the U‐Nets. A separate validation set with 15 whole breast volumes was used for hyperparameter tuning. We performed Kruskal–Walis test on the results of our hyperparameter tuning and did not find a statistically significant difference in the ten top models of each study. For this reason, we chose the best model as the model with the highest mean dice similarity coefficient (DSC) value on the validation set. The reported test results are the results of the top model of each study on our test set which contained 19 whole breast volumes annotated by three readers fused with the STAPLE algorithm. We also investigated the effect of the quality of the training annotations and the number of training samples for this task. Results The study with the highest average DSC result was 3D‐MULTI with 0.96 ± 0.02. The second highest average is 2D WOFS (0.96 ± 0.03), and the third is 2D MULTI (0.96 ± 0.03). We performed the Kruskal–Wallis one‐way ANOVA test with Dunn's multiple comparison tests using Bonferroni P‐value correction on the results of the selected model of each study and found that 3D‐MULTI, 2D‐MULTI, 3D‐WOFS, 2D‐WOFS, 2D‐FS, and 3D‐FS were not statistically different in their distributions, which indicates that comparable results could be obtained in fat‐suppressed and nonfat‐suppressed volumes and that there is no significant difference between the 3D and 2D approach. Our results also suggested that the networks trained on single sequence images or multiple sequence images organized in multichannel i...

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Section: Discussionsupporting

confidence: 90%

Section: Discussionmentioning

confidence: 99%

Section: Background and Introductionmentioning

confidence: 99%

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An investigation of the effect of fat suppression and dimensionality on the accuracy of breast MRI segmentation using U‐nets

Fashandi

Kuling

et al. 2019

Medical Physics

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“…They also tried to aggregate contextual information by utilizing atrous convolution or pooling at different rates . Some papers reported organ segmentation using medical images . Sahiner et al investigated the deep learning in medical image segmentation.…”

Section: Introductionmentioning

confidence: 99%

Endometrium segmentation on transvaginal ultrasound image using key‐point discriminator

et al. 2019

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Purpose Transvaginal ultrasound imaging provides useful information for diagnosing endometrial pathologies and reproductive health. Endometrium segmentation in transvaginal ultrasound (TVUS) images is very challenging due to ambiguous boundaries and heterogeneous textures. In this study, we developed a new segmentation framework which provides robust segmentation against ambiguous boundaries and heterogeneous textures of TVUS images. Methods To achieve endometrium segmentation from TVUS images, we propose a new segmentation framework with a discriminator guided by four key points of the endometrium (namely, the endometrium cavity tip, the internal os of the cervix, and the two thickest points between the two basal layers on the anterior and posterior uterine walls). The key points of the endometrium are defined as meaningful points that are related to the characteristics of the endometrial morphology, namely the length and thickness of the endometrium. In the proposed segmentation framework, the key‐point discriminator distinguishes a predicted segmentation map from a ground‐truth segmentation map according to the key‐point maps. Meanwhile, the endometrium segmentation network predicts accurate segmentation results that the key‐point discriminator cannot discriminate. In this adversarial way, the key‐point information containing endometrial morphology characteristics is effectively incorporated in the segmentation network. The segmentation network can accurately find the segmentation boundary while the key‐point discriminator learns the shape distribution of the endometrium. Moreover, the endometrium segmentation can be robust to the heterogeneous texture of the endometrium. We conducted an experiment on a TVUS dataset that contained 3,372 sagittal TVUS images and the corresponding key points. The dataset was collected by three hospitals (Ewha Woman’s University School of Medicine, Asan Medical Center, and Yonsei University College of Medicine) with the approval of the three hospitals’ Institutional Review Board. For verification, fivefold cross‐validation was performed. Result The proposed key‐point discriminator improved the performance of the endometrium segmentation, achieving 82.67 % for the Dice coefficient and 70.46% for the Jaccard coefficient. In comparison, on the TVUS images UNet, showed 58.69 % for the Dice coefficient and 41.59 % for the Jaccard coefficient. The qualitative performance of the endometrium segmentation was also improved over the conventional deep learning segmentation networks. Our experimental results indicated robust segmentation by the proposed method on TVUS images with heterogeneous texture and unclear boundary. In addition, the effect of the key‐point discriminator was verified by an ablation study. Conclusion We proposed a key‐point discriminator to train a segmentation network for robust segmentation of the endometrium with TVUS images. By utilizing the key‐point information, the proposed method showed more reliable and accurate segmentation performance and outperformed the con...

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“…Knowledge‐based methods use intensity operations and gradient signs, edge properties, or a priori atlases . Deep learning‐based methods for chest wall segmentation have used artificial neural networks in the form of convolutional neural networks . The performance of these methods is difficult to compare as for each method results have been reported for different data sets, which vary widely in the number of ACR 4 images included.…”

Section: Introductionmentioning

confidence: 99%

Knowledge‐based and deep learning‐based automated chest wall segmentation in magnetic resonance images of extremely dense breasts

et al. 2019

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Purpose Segmentation of the chest wall, is an important component of methods for automated analysis of breast magnetic resonance imaging (MRI). Methods reported to date show promising results but have difficulties delineating the muscle border correctly in breasts with a large proportion of fibroglandular tissue (i.e., dense breasts). Knowledge‐based methods (KBMs) as well as methods based on deep learning have been proposed, but a systematic comparison of these approaches within one cohort of images is currently lacking. Therefore, we developed a KBM and a deep learning method for segmentation of the chest wall in MRI of dense breasts and compared their performances. Methods Two automated methods were developed, an optimized KBM incorporating heuristics aimed at shape, location, and gradient features, and a deep learning‐based method (DLM) using a dilated convolution neural network. A data set of 115 T1‐weighted MR images was randomly selected from MR images of women with extremely dense breasts (ACR BI‐RADS category 4) participating in a screening trial of women (mean age 56.6 yr, range 49.5–75.2 yr) with dense breasts. Manual segmentations of the chest wall, acquired under supervision of an experienced breast radiologist, were available for all data sets. Both methods were optimized using the same randomly selected 36 MRI data sets from a total of 115 data sets. Each MR data set consisted of 179 transversal images with voxel size 0.64 mm3 × 0.64 mm3 × 1.00 mm3. In the remaining 79 data sets, the results of both segmentation methods were qualitatively evaluated. A radiologist reviewed the segmentation results of both methods in all transversal images (n = 14 141) and determined whether the result would impact the ability to accurately determine the volume of fibroglandular and fatty tissue and whether segmentations masked breast regions that might harbor lesions. When no relevant deviation was detected, the result was considered successful. In addition, all segmentations were quantitatively assessed using the Dice similarity coefficient (DSC) and Hausdorff distance (HD), 95th percentile of the Hausdorff distance (HD95), false positive fraction (FPF), and false negative fraction (FNF) metrics. Results According to the radiologist's evaluation, the DLM had a significantly higher success rate than the KBM (81.6% vs 78.4%, P < 0.01). The success rate was further improved to 92.1% by combining both methods. Similarly, the DLM had significantly lower values for FNF (0.003 ± 0.003 vs 0.009 ± 0.011, P < 0.01) and HD95 (2.58 ± 1.78 mm vs 3.37 ± 2.11, P < 0.01). However, the KBM resulted in a significantly lower FPF than the DLM (0.018 ± 0.009 vs 0.030 ± 0.009, P < 0.01).There was no significant difference between the KBM and DLM in terms of DSC (0.982 ± 0.006 vs 0.984 ± 0.008, P = 0.08) or HD (24.14 ± 20.69 mm vs 12.81 ± 27.28 mm, P = 0.05). Conclusion Both optimized knowledge‐based and DLM showed good results to segment the pectoral muscle in women with dense breasts. Qualitatively assessed, the DLM was the most robust...

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Using deep learning to segment breast and fibroglandular tissue in MRI volumes

Cited by 193 publications

References 29 publications

An investigation of the effect of fat suppression and dimensionality on the accuracy of breast MRI segmentation using U‐nets

An investigation of the effect of fat suppression and dimensionality on the accuracy of breast MRI segmentation using U‐nets

Endometrium segmentation on transvaginal ultrasound image using key‐point discriminator

Knowledge‐based and deep learning‐based automated chest wall segmentation in magnetic resonance images of extremely dense breasts

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