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An Investigation into Brain Tumor Segmentation Techniques IIRindia
A tumor is an anomalous mass in the brain which can be cancerous. Such anomalous growth within this restricted space or inside the covering skull can cause problems. Detecting brain tumors from images of medical modalities like CT scan or MRI involves segmentation (Division into parts) for analysis and can be a challenging task. Accurate segmentation of brain images is very essential for proper diagnosis of tumor and non-tumor areas for clinical analysis. This paper details on segmentation algorithms for brain images, advantages, disadvantages and a comparison of the algorithms.
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Slides presented at International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI), 2022, Singapore. DOI: 10.1007/978-3-031-16443-9_13
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Brain image classification is one of the utmost imperative parts of clinical investigative tools. Brain images
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images, which presents a substantial challenge in image segmentation. The most extensively used image
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[MICCAI 2021] MT-UDA: Towards unsupervised cross-modality medical image segmentation with limited source labels
1. MT-UDA: Towards Unsupervised Cross-modality Medical Image
Segmentation with Limited Source Labels
Ziyuan Zhao1,2, Kaixin Xu2, Shumeng Li1,2, Zeng Zeng2, Cuntai Guan1
Presenter: Zhao Ziyuan (G2104205L)
1 Nanyang Technological University 2 Institute for Infocomm Research, A*STAR
Th-S2: Image Segmentation + Domain Adaptation # 678
2. Introduction – Medical Image Segmentation
- Segmentation in medical physics plays a crucial role in medical image analysis (MedIA)
- For instance, left atrium (LA) segmentation can provide a pre-operative assessment of its anatomy,
which is essential for treating various cardiovascular diseases, such as atrial fibrillation
- Deep learning has been widely used for medical image segmentation
Left Atrial Cavity 3D LA Visualization
2018 Atrial Segmentation Challenge. https://atriaseg2018.cardiacatlas.org/
3. Challenge (1) – Label Scarcity
- DCNNs are data-hungry and require large amounts of well-annotated data.
- Annotating medical images is laborious, expensive, and requires human expertise → Label Scarcity
Time & money
consuming
Knowledge-driven
Labor-intensive
Image DCNNs Segmentation map
4. Challenge (2) – Domain Shift
- In real-world clinical scenarios, medical images are acquired with different physical principles and
modalities, e.g., MRI & CT→ different visual appearance & distribution (Domain Gap)
- DCNNs suffer from severe performance degradation when domain shift (e.g., CT → MRI)
Cardiac Label
Cardiac CT
Source Domain
Annotate
DCNNs
Transfer
Cardiac MR
Feed
Domain Gap
Target Domain
5. Existing Work – Unsupervised Domain Adaptation
- Image adaptation – Cycle GAN (2017)
- Feature adaptation – ADDA (2017)
- Sequentially combine two adaptive strategies – CyCADA (2018)
- Synergistic Image and Feature Adaptation – SIFA (2019, 2020)
Cardiac CT Cardiac MR
Domain Gap
CycleGAN [1]
ADDA [2]
Cycada [3]
SIFA [4]
[1] Zhu, Jun-Yan, et al. "Unpaired image-to-image translation using cycle-consistent adversarial networks." ICCV 2017
[2] Tzeng, Eric, et al. "Adversarial discriminative domain adaptation." CVPR 2017.
[3] Hoffman, Judy, et al. "Cycada: Cycle-consistent adversarial domain adaptation.“ ICML 2018
[4] Chen, Cheng, et al. "Unsupervised bidirectional cross-modality adaptation via deeply synergistic image and feature alignment for medical image segmentation." TMI 2020
Source Domain Target Domain
6. Problem – Source Label Scarcity
- Despite the success of adversarial learning in UDA, these methods heavily rely on abundant source
labels.
- Become sub-optimal when only limited source labels are available in clinical deployment.
- Motivates us to study a challenging UDA scenario – source label scarcity
Source Label Scarcity
Cardiac CT Cardiac MR
Domain Gap
Source Domain Target Domain
Less
Annotations
Lower
Performance
7. Motivation – SSL + UDA
- Image-level adaptation → generates a lot of synthetic images with abundant information, which can be
leveraged for semi-supervised learning
- Appearance consistency → synthetic and real images from the same domain maintain a similar visual
appearance
- Structural consistency → transformed images should have the same structural information as the
original ones
Zhao, Ziyuan, et al. "MT-UDA: Towards Unsupervised Cross-modality Medical Image Segmentation with Limited Source Labels." MICCAI 2021
SSL
Labeled data
Performance
Number of labeled data
DCNNs
Source Domain
Target Domain
Style Transfer
8. Method – MT-UDA
- Investigate the feasibility of integrating SSL into UDA under source label scarcity
- Develop a label-efficient UDA framework based on mean teacher (MT) to explore the knowledge from
both domains
Zhao, Ziyuan, et al. "MT-UDA: Towards Unsupervised Cross-modality Medical Image Segmentation with Limited Source Labels." MICCAI 2021
9. Method (1)– Dual Cycle Alignment Module
- Generate synthetic samples for two domains using generative adversarial networks
- Synthesize source-like domain images and target-like domain images via adversarial learning
Zhao, Ziyuan, et al. "MT-UDA: Towards Unsupervised Cross-modality Medical Image Segmentation with Limited Source Labels." MICCAI 2021
10. Method (2)– Semantic Knowledge Transfer
- Appearance consistency → synthetic and real images from the same domain maintain a similar visual
appearance
- Employ the mean teacher (MT) model to distill the intra-domain semantic knowledge by forcing the
prediction consistency
Zhao, Ziyuan, et al. "MT-UDA: Towards Unsupervised Cross-modality Medical Image Segmentation with Limited Source Labels." MICCAI 2021
11. Method (3)– Structural Knowledge Transfer
- Structural consistency → transformed images should have the same structural information as the
original ones
- Propose a teacher model for keeping structural consistency between predictions of source images and
corresponding synthetic target images
Zhao, Ziyuan, et al. "MT-UDA: Towards Unsupervised Cross-modality Medical Image Segmentation with Limited Source Labels." MICCAI 2021
12. Experiments
- Dataset
- Multi-Modality Whole Heart Segmentation (MM-WHS) 2017 dataset
- Unpaired 20 MR and 20 CT volumes with ground truth masks
- Data preprocessing
- MR → source domain, CT → target domain
- SIFA setting (-16) → 16: 4 random split for train / val
- Our setting (-4)→ 4 MR volumes are labelled for UDA under source label scarcity
- Images were cropped into the size of 256 x 256
- Implementation details
- Test on fake MR images generated from CT
- Backbone: U-Net
- Supervised loss: Dice + Cross-entropy
- Total loss:
Zhao, Ziyuan, et al. "MT-UDA: Towards Unsupervised Cross-modality Medical Image Segmentation with Limited Source Labels." MICCAI 2021
13. Results (1) – Quantitative Comparison
- Degraded performance on target domain when using 4 labeled source domain scans
- MT and UA-MT can help improve the segmentation performance on target domain
- Demonstrate the feasibility of integrating SSL into UDA for label-efficient UDA
suffix −4 or −16
# labelled source scans
used for training
[1] Dou, Qi, et al. "Unsupervised cross-modality domain adaptation of convnets for biomedical image segmentations with adversarial loss." IJCAI 2018
[2] Chen, Cheng, et al. "Synergistic image and feature adaptation: Towards cross-modality domain adaptation for medical image segmentation." AAAI 2019
[3] Chen, Cheng, et al. "Unsupervised bidirectional cross-modality adaptation via deeply synergistic image and feature alignment for medical image segmentation." TMI 2020
[4] Tarvainen, Antti, and Harri Valpola. "Mean teachers are better role models: Weight-averaged consistency targets improve semi-supervised deep learning results.“NIPS 2017
[5] Yu, Lequan, et al. "Uncertainty-aware self-ensembling model for semi-supervised 3D left atrium segmentation.“ MICCAI 2019
14. Results (2) – Qualitative Comparison
- It is observed that our method can generate more reliable masks with fewer false positives
Zhao, Ziyuan, et al. "MT-UDA: Towards Unsupervised Cross-modality Medical Image Segmentation with Limited Source Labels." MICCAI 2021
15. Results (3) – Ablation Study
- Remove one of the teacher models, separately
- W/o semantic knowledge transfer (MT-UDA-NS)
- W/o structural knowledge transfer (MTUDA-NT)
- Replace structural consistency loss with MSE loss (MT-UDA-NS-MSE)
Zhao, Ziyuan, et al. "MT-UDA: Towards Unsupervised Cross-modality Medical Image Segmentation with Limited Source Labels." MICCAI 2021
16. Conclusions
- Study a practical, challenging, and different UDA setting from the past, where only limited source labels
are accessible → Source Label Scarcity
- Investigate the feasibility of integrating SSL into UDA under source label scarcity
- Propose a label-efficient UDA framework for cross-modality medical image segmentation
- Leverage intra-domain semantic knowledge and exploit inter-domain structural knowledge
concurrently, thereby mitigating both the domain discrepancy and source label scarcity.
Zhao, Ziyuan, et al. "MT-UDA: Towards Unsupervised Cross-modality Medical Image Segmentation with Limited Source Labels." MICCAI 2021