Medical image segmentation is important for identifying human organs, essential in clinical diagnosis and treatment planning.However, the accuracy of segmentation results is often compromised due to the limited quality and completeness of medical imaging data. In practical applications, deep learning has become a key method for multiorgan segmentation[1, 3], but it struggles with challenges related to the amount and quality of data.Deep learning segmentation models typically require numerous paired images and annotations for training[2]. However, fully annotated multi-organ CT datasets are rare, while those annotating only a few organs are more frequent. The variation in annotations restricts the efficient utilization of numerous public segmentation datasets. Inspired by disentangled learning’s ability to share knowledge across tasks[4, 5, 6], we’ve developed a method that allows models to learn and incorporate features from different datasets. We attempt to combine two types of datasets: one fully annotated for multiple organs but with a small amount of data, and another larger dataset annotated only for certain organs.This method is designed to improve the model’s capability in segmenting multiple organs.Using disentangled learning, the model is able to extract and combine crucial features from various datasets, thus overcoming the challenge of inconsistent annotations. This method aims to enhance the model’s adaptability and precision. We assess its performance by comparing the model’s predicted segmentations with actual annotations, allowing for a detailed evaluation of using the disentangled learning approach versus models trained with only a single dataset in multi-organ segmentation tasks. To summarize, the thesis will cover the following aspects:
- Design a multi-organ segmentation model using disentangled learning methods.
- Investigate the influence of the quantity of fused datasets on the multiorgan segmentation model.
- Investigate the influence of the proportion of data quantity from different datasets on the multi-organ segmentation model.
- Investigate the influence of feature weights from different datasets on the multi-organ segmentation model.
References
[1] Yabo Fu, Yang Lei, TongheWang, Walter J. Curran, Tian Liu, and Xiaofeng Yang. A review of deep learning based methods for medical image multiorgan segmentation. Physica Medica, 85:107–122, 2021.
[2] Tianxing He, Shengcheng Yu, Ziyuan Wang, Jieqiong Li, and Zhenyu Chen. From data quality to model quality: an exploratory study on deep learning, 2019.
[3] Yang Lei, Yabo Fu, Tonghe Wang, Richard L. J. Qiu, Walter J. Curran, Tian Liu, and Xiaofeng Yang. Deep learning in multi-organ segmentation, 2020.
[4] Yuanyuan Lyu, Haofu Liao, Heqin Zhu, and S. Kevin Zhou. A3dsegnet: Anatomy-aware artifact disentanglement and segmentation network for unpaired segmentation, artifact reduction, and modality translation, 2021.
[5] Qiushi Yang, Xiaoqing Guo, Zhen Chen, Peter Y. M. Woo, and Yixuan Yuan. D2-net: Dual disentanglement network for brain tumor segmentation with missing modalities. IEEE Transactions on Medical Imaging, 41(10):2953–2964, 2022.
[6] Tongxue Zhou, Su Ruan, and St´ephane Canu. A review: Deep learning for medical image segmentation using multi-modality fusion. Array, 3-4:100004, 2019.