Automated deep learning method for whole-breast segmentation in diffusion-weighted breast MRI

Abstract

Background: Diffusion-weighted imaging (DWI) in MRI plays an increasingly important role in diagnostic applications and developing imaging biomarkers. Automated whole-breast segmentation is an important yet challenging step for quantitative breast imaging analysis. While methods have been developed on dynamic contrast-enhanced (DCE) MRI, automatic whole-breast segmentation in breast DWI MRI is still underdeveloped.

Purpose: To develop a deep/transfer learning-based segmentation approach for DWI MRI scans and conduct an extensive study assessment on four imaging datasets from both internal and external sources.

Study type: Retrospective.

Subjects: In all, 98 patients (144 MRI scans; 11,035 slices) of four different breast MRI datasets from two different institutions.

Field strength/sequences: 1.5T scanners with DCE sequence (Dataset 1 and Dataset 2) and DWI sequence. A 3.0T scanner with one external DWI sequence.

Assessment: Deep learning models (UNet and SegNet) and transfer learning were used as segmentation approaches. The main DCE Dataset (4,251 2D slices from 39 patients) was used for pre-training and internal validation, and an unseen DCE Dataset (431 2D slices from 20 patients) was used as an independent test dataset for evaluating the pre-trained DCE models. The main DWI Dataset (6,343 2D slices from 75 MRI scans of 29 patients) was used for transfer learning and internal validation, and an unseen DWI Dataset (10 2D slices from 10 patients) was used for independent evaluation to the fine-tuned models for DWI segmentation. Manual segmentations by three radiologists (>10-year experience) were used to establish the ground truth for assessment. The segmentation performance was measured using the Dice Coefficient (DC) for the agreement between manual expert radiologist's segmentation and algorithm-generated segmentation.

Statistical tests: The mean value and standard deviation of the DCs were calculated to compare segmentation results from different deep learning models.

Results: For the segmentation on the DCE MRI, the average DC of the UNet was 0.92 (cross-validation on the main DCE dataset) and 0.87 (external evaluation on the unseen DCE dataset), both higher than the performance of the SegNet. When segmenting the DWI images by the fine-tuned models, the average DC of the UNet was 0.85 (cross-validation on the main DWI dataset) and 0.72 (external evaluation on the unseen DWI dataset), both outperforming the SegNet on the same datasets.

Data conclusion: The internal and independent tests show that the deep/transfer learning models can achieve promising segmentation effects validated on DWI data from different institutions and scanner types. Our proposed approach may provide an automated toolkit to help computer-aided quantitative analyses of breast DWI images.

Level of evidence: 3 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2020;51:635-643.

Keywords: breast MRI; breast segmentation; deep learning; diffusion-weighted imaging.

FIGURE 1:

Comparison of breast DWI (top row) and DCE (bottom row) MRI images from four different patients. Each column is a DWI center slice and a DCE center slice from the same patient. DWI images have an overall lower imaging quality (e.g., blurred appearance, more noise/artifacts, and lower resolution) and different fields of view in comparison with the DCE images, posing additional challenges for automated whole-breast segmentation in DWI images than in DCE images.

FIGURE 2:

Breast segmentation method development and evaluation pipeline.

FIGURE 3:

Selected segmentation results from four different patients using UNet and SegNet on Dataset 1. As can be seen, the UNet model yielded fewer false positives than the SegNet when compared their segmentations with the manual segmentation in DCE MRI.

FIGURE 4:

Selected segmentation results from four different patients using UNet and SegNet on independent never-seen Dataset 2, still showing encouraging segmentation performance.

FIGURE 5:

Dice Coefficient (a) and training loss (b) curves during training the UNet model on DWI Dataset 3 (training from scratch vs. fine-tuning).

FIGURE 6:

Selected segmentation results from four different patients using UNet and SegNet on Dataset 3. In this evaluation the UNet model performs better than the SegNet in the DWI image segmentation.

FIGURE 7:

Selected segmentation results from four different patients using UNet and SegNet on external never-seen Dataset 4. Although the image quality is poor, we can observe that the UNet and the SegNet trained by the proposed pipeline can still capture the shape of the whole-breast in DWI MRI, where the results of the UNet model are more visually reasonable.

FIGURE 8:

Boxplots of the Dice Coefficient distribution for the fine-tuned UNet and SegNet evaluated on DWI Dataset 3 (a) and DWI Dataset 4 (b).