Organizations: Center for Precision Medicine, The First Affiliated Hospital of Xiamen University, School of Medicine, Xiamen University, Xiamen, China; Department of Laboratory Medicine, The First Affiliated Hospital of Xiamen University, School of Medicine, Xiamen University, Xiamen, 361003, China · Maynooth International Engineering College, Fuzhou University, Fuzhou, 350108, China · Department of Computer and Data Science, Case Western Reserve University, Cleveland, Ohio, USA, Cleveland, 44106, Ohio, USA · Department of Rehabilitation Medicine, the First Affiliated Hospital of Xiamen University, School of Medicine, Xiamen University, Xiamen, 361003, China · Department of Pharmacy, Xiang’an Hospital of Xiamen University, School of Medicine, Xiamen University, Xiamen, 361005, China
Breast cancer is one of the most common causes of death among women worldwide, with millions of fatalities annually. Magnetic Resonance Imaging (MRI) can provide various sequences for characterizing tumor morphology and internal patterns, and becomes an effective tool for detection and diagnosis of breast tumors. However, previous deep-learning based tumor segmentation methods have limitations in accurately locating tumor contours due to the challenge of low contrast between cancer and normal areas and blurred boundaries. Leveraging text prompt information holds promise in ameliorating tumor segmentation effect by delineating segmentation regions. Inspired by this, we propose text-guided Breast Tumor Segmentation model (TextBCS) with stage-divided vision-language interaction and evidential learning. Specifically, the proposed stage-divided vision-language interaction facilitates information mutual between visual and text features at each stage of down-sampling, further exerting the advantages of text prompts to assist in locating lesion areas in low contrast scenarios. Moreover, the evidential learning is adopted to quantify the segmentation uncertainty of the model for blurred boundary. It utilizes the variational Dirichlet to characterize the distribution of the segmentation probabilities, addressing the segmentation uncertainties of the boundaries. Extensive experiments validate the superiority of our TextBCS over other segmentation networks, showcasing the best breast tumor segmentation performance on publicly available datasets.
Figures & tables
Figure 1: Various segmentation methods for breast cancer segmentation. (a) Traditional segmentation methods rely solely on the image to perform segmentation tasks. (b) Our TextBCS leverages text prompts related to canceration regions to assist in locating breast cancer areas.
Figure 2: Overview of TextBCS, consisting of the SVLI module and the evidential learning module. SVLI module performs cross fusion and alignment between the image and text feature at each downsampling stage, enhancing sufficient image-text interaction. The evidential learning module performs pixel-level uncertainty estimation based on the decoding results, preventing the model from making unreliable predictions.
Methods
Text
Dice (%)
mIoU (%)
Param (M)
Flops (G)
UNet [ 39 ]
✗
81.54
73.22
14.8
50.3
UNet++ [ 64 ]
✗
81.23
72.69
74.5
94.6
nnUNet [ 16 ]
✗
80.15
71.81
19.1
412.7
TransUNet [ 7 ]
✗
83.14
75.49
105
56.7
SwinUNet [ 5 ]
✗
81.70
70.25
82.3
67.3
UCTransNet [ 51 ]
✗
78.07
69.41
65.6
63.2
Table 1: The quantitative comparison between our method and other comparison methods on the Duke-Breast-Cancer-MRI dataset demonstrates the superiority of our method. The best values are in bold.
Baseline
SVLI
EL
Dice (%)
mIoU (%)
✓
81.54
73.22
✓
✓
84.41
75.24
✓
✓
83.19
74.74
✓
✓
✓
85.33
76.08
Table 2: Ablation studies on the Duke-Breast-Cancer-MRI dataset demonstrate the significant improvements of the proposed innovations, including SVLI and EL. The best values are in bold.
Figure 3: The visual comparison of segmentation results between different methods,such as UNet, nnUNet, TransUNet, CLIP, MGCA and LViT, is displayed. Each row corresponds to one subject.
Figure 4: Saliency map for interpretability study of different layers in encoder. It is obvious that the introduction of interaction between text prompts and images in each stage can better locate the breast cancer region.
Figure 5: A case that our method fail under the text prompt by segmenting incorrect areas and insufficiently identifying the target area.
Coefficient
1.0
0.1
0.01
0.001
λ1
84.79
84.83
85.33
85.14
λ3
85.11
84.90
85.33
85.07
Table 3: Ablation study of parameters ( λ1,λ3 ). Dice values of Duke-Breast-Cancer-MRI datasets are reported. Bold values are the Dice values corresponding to the optimal parameters.
Methods
p-values
Unet [ 39 ]
0.0014
Unet++ [ 64 ]
0.0032
nnUNet [ 16 ]
0.0008
TransUNet [ 7 ]
0.0030
SwinUNet [ 5 ]
0.0018
UCTransNet [ 51 ]
0.0009
Table 4: P-values from t-test between our method and other methods shown that our method exhibits statistical significance. P-values are calculated based on the Dice.
Figure 6: Qualitative experiment of different styles of text prompts shows the robustness of the model to variations in the text prompts.