| 1. |
- Tomic, Hanna, et al.
(författare)
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Using simulated breast lesions based on Perlin noise for evaluation of lesion segmentation
- 2024
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Ingår i: Medical Imaging 2024 : Physics of Medical Imaging - Physics of Medical Imaging. - : SPIE-Intl Soc Optical Eng. - 1605-7422. - 9781510671546 ; 12925
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Konferensbidrag (refereegranskat)abstract
- Segmentation of diagnostic radiography images using deep learning is progressively expanding, which sets demands on the accessibility, availability, and accuracy of the software tools used. This study aimed at evaluating the performance of a segmentation model for digital breast tomosynthesis (DBT), with the use of computer-simulated breast anatomy. We have simulated breast anatomy and soft tissue breast lesions, by utilizing a model approach based on the Perlin noise algorithm. The obtained breast phantoms were projected and reconstructed into DBT slices using a publicly available open-source reconstruction method. Each lesion was then segmented using two approaches: 1. the Segment Anything Model (SAM), a publicly available AI-based method for image segmentation and 2. manually by three human observers. The lesion area in each slice was compared to the ground truth area, derived from the binary mask of the lesion model. We found similar performance between SAM and manual segmentation. Both SAM and the observers performed comparably in the central slice (mean absolute relative error compared to the ground truth and standard deviation SAM: 4 ± 3 %, observers: 3 ± 3 %). Similarly, both SAM and the observers overestimated the lesion area in the peripheral reconstructed slices (mean absolute relative error and standard deviation SAM: 277 ± 190 %, observers: 295 ± 182 %). We showed that 3D voxel phantoms can be used for evaluating different segmentation methods. In preliminary comparison, tumor segmentation in simulated DBT images using SAM open-source method showed a similar performance as manual tumor segmentation.
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| 2. |
- Yang, Zhikai, et al.
(författare)
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3D Breast Ultrasound Image Classification Using 2.5D Deep learning
- 2024
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Ingår i: 17th International Workshop on Breast Imaging, IWBI 2024. - : SPIE-Intl Soc Optical Eng.
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Konferensbidrag (refereegranskat)abstract
- The 3D breast ultrasound is a radiation-free and effective imaging technology for breast tumor diagnosis. However, checking the 3D breast ultrasound is time-consuming compared to mammograms. To reduce the workload of radiologists, we proposed a 2.5D deep learning-based breast ultrasound tumor classification system. First, we used the pre-trained STU-Net to finetune and segment the tumor in 3D. Then, we fine-tuned the DenseNet-121 for classification using the 10 slices with the biggest tumoral area and their adjacent slices. The Tumor Detection, Segmentation, and Classification on Automated 3D Breast Ultrasound (TDSC-ABUS) MICCAI Challenge 2023 dataset was used to train and validate the performance of the proposed method. Compared to a 3D convolutional neural network model and radiomics, our proposed method has better performance.
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| 3. |
- Yang, Zhikai, et al.
(författare)
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Lesion Localization in Digital Breast Tomosynthesis with Deformable Transformers by Using 2.5D Information
- 2024
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Ingår i: Medical Imaging 2024: Computer-Aided Diagnosis. - : SPIE-Intl Soc Optical Eng.
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Konferensbidrag (refereegranskat)abstract
- In this study, we adapted a transformer-based method to localize lesions in digital breast tomosynthesis (DBT) images. Compared with convolutional neural network-based object detection methods, the transformer-based method does not require non-maximum suppression postprocessing. Integrated deformable convolution detection transformers can better capture small-size lesions. We added transfer learning to tackle the issue of the lack of annotated data from DBT. To validate the superiority of the transformer-based detection method, we compared the results with deep-learning object detection methods. The experimental results demonstrated that the proposed method performs better than all comparison methods.
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