Automated Breast Tumor Segmentation in DCE-MRI Using Deep Learning

Benjelloun, M.; Adoui, Mohammed El; Larhmam, Mohamed Amine; Mahmoudi, Sidi Ahmed

doi:10.1109/cloudtech.2018.8713352

Cited by 33 publications

(15 citation statements)

References 11 publications

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“…Breast tumor segmentation based on DCE-MRI remains an important task for several breast tumor routines. For example, for tumor-response prediction to chemotherapy, it is necessary to go through a tumor segmentation step [8][9][10]. Currently, many manual or semi-automatic tumor annotation Breast tumor segmentation based on DCE-MRI remains an important task for several breast tumor routines.…”

Section: Introductionmentioning

confidence: 99%

“…Currently, many manual or semi-automatic tumor annotation Breast tumor segmentation based on DCE-MRI remains an important task for several breast tumor routines. For example, for tumor-response prediction to chemotherapy, it is necessary to go through a tumor segmentation step [8][9][10]. Currently, many manual or semi-automatic tumor annotation techniques are used [11,12].…”

Section: Introductionmentioning

confidence: 99%

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MRI Breast Tumor Segmentation Using Different Encoder and Decoder CNN Architectures

et al. 2019

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Breast tumor segmentation in medical images is a decisive step for diagnosis and treatment follow-up. Automating this challenging task helps radiologists to reduce the high manual workload of breast cancer analysis. In this paper, we propose two deep learning approaches to automate the breast tumor segmentation in dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) by building two fully convolutional neural networks (CNN) based on SegNet and U-Net. The obtained models can handle both detection and segmentation on each single DCE-MRI slice. In this study, we used a dataset of 86 DCE-MRIs, acquired before and after two cycles of chemotherapy, of 43 patients with local advanced breast cancer, a total of 5452 slices were used to train and validate the proposed models. The data were annotated manually by an experienced radiologist. To reduce the training time, a high-performance architecture composed of graphic processing units was used. The model was trained and validated, respectively, on 85% and 15% of the data. A mean intersection over union (IoU) of 68.88 was achieved using SegNet and 76.14% using U-Net architecture.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

MRI Breast Tumor Segmentation Using Different Encoder and Decoder CNN Architectures

et al. 2019

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“…Manual segmentation precludes the creation of a fully automated lesion characterization pipeline. Given the variability in bone lesion location and the non-uniform shape of bones based on anatomical location, automated bone lesion segmentation would ostensibly be a much more challenging task than, for example, automated segmentation of the breast or brain tumors on MRI, both of which have been previously demonstrated [ 45 , 46 ]. While automated segmentation of specific osseous structures has been demonstrated, such as the proximal and distal femur and the proximal tibia, fully automated segmentation of bone lesions has not been reported [ 47 , 48 ].…”

Section: Discussionmentioning

confidence: 99%

Deep Learning for Classification of Bone Lesions on Routine MRI

Eweje

Bao

et al. 2021

EBioMedicine

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Background Radiologists have difficulty distinguishing benign from malignant bone lesions because these lesions may have similar imaging appearances. The purpose of this study was to develop a deep learning algorithm that can differentiate benign and malignant bone lesions using routine magnetic resonance imaging (MRI) and patient demographics. Methods 1,060 histologically confirmed bone lesions with T1- and T2-weighted pre-operative MRI were retrospectively identified and included, with lesions from 4 institutions used for model development and internal validation, and data from a fifth institution used for external validation. Image-based models were generated using the EfficientNet-B0 architecture and a logistic regression model was trained using patient age, sex, and lesion location. A voting ensemble was created as the final model. The performance of the model was compared to classification performance by radiology experts. Findings The cohort had a mean age of 30±23 years and was 58.3% male, with 582 benign lesions and 478 malignant. Compared to a contrived expert committee result, the ensemble deep learning model achieved (ensemble vs. experts): similar accuracy (0·76 vs. 0·73, p=0·7), sensitivity (0·79 vs. 0·81, p=1·0) and specificity (0·75 vs. 0·66, p=0·48), with a ROC AUC of 0·82. On external testing, the model achieved ROC AUC of 0·79. Interpretation Deep learning can be used to distinguish benign and malignant bone lesions on par with experts. These findings could aid in the development of computer-aided diagnostic tools to reduce unnecessary referrals to specialized centers from community clinics and limit unnecessary biopsies. Funding This work was funded by a Radiological Society of North America Research Medical Student Grant (#RMS2013) and supported by the Amazon Web Services Diagnostic Development Initiative.

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“…In order to successfully detect and segment each individual breast slice in the DCE-MRI breast tumor dataset, Benjelloun et al [18] developed a fully convolutional neural network architecture based on U-Net [2] for the first time. After that, some studies tried to combine the low-level feature of the shallow layer with the high-level feature of the deep layer to take full advantage of multi-scale information and ameliorate detail restoration problem.…”

Section: A Medical Image Segmentation Based On Cnnsmentioning

confidence: 99%

EG-TransUNet: Enhanced and Guided U-Net with Transformer for Biomedical Image Segmentation

Pan

Liu

Xie

et al. 2022

Preprint

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Although methods based on convolutional neural network have improved the performance of biomedical image segmentation, to meet the precision requirements of medical imaging segmentation task, the medical image segmentation method based on the deep learning still need to solve the following problems: 1) It is difficult to extract the discriminative feature of the lesion region in medical images during the encoding caused by variable sizes and shapes; 2) It is difficult to fuse the spatial and semantic information of the lesion region effectively during the decoding caused by redundant information and semantic gap. In this paper, we propose a novel U-Net variant architecture called EG-TransUNet, which is able to improve the feature discrimination at the level of spatial detail and semantic location using progressive enhancement module (PEM) and channel spatial attention (CSA) based on self-attention and effectively fuse the spatial and semantic information using semantic guidance attention (SGA). The proposed EG-TransUNet allows to capture object variabilities and provides improved results on different biomedical datasets. Extensive experiments on EG-TransUNet demonstrate that the method advances the performance on five publicly available segmentation datasets, and also, is more generalizable as compared to state-of-the-art methods.

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Automated Breast Tumor Segmentation in DCE-MRI Using Deep Learning

Cited by 33 publications

References 11 publications

MRI Breast Tumor Segmentation Using Different Encoder and Decoder CNN Architectures

MRI Breast Tumor Segmentation Using Different Encoder and Decoder CNN Architectures

Deep Learning for Classification of Bone Lesions on Routine MRI

EG-TransUNet: Enhanced and Guided U-Net with Transformer for Biomedical Image Segmentation

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