Bottleneck feature supervised U-Net for pixel-wise liver and tumor segmentation
Related Research Unit(s)
|Journal / Publication||Expert Systems with Applications|
|Online published||11 Dec 2019|
|Publication status||Published - 1 May 2020|
|Link to Scopus||https://www.scopus.com/record/display.uri?eid=2-s2.0-85076777228&origin=recordpage|
Liver cancer is one of the most common cancer types with high death rate. Doctors diagnose cancer by examining the CT images, which can be time-consuming and prone to error. Therefore, an automatic segmentation method is desired for clinical practice. In the literature, many U-Net-based models were proposed. But few of them focus on the bottleneck feature vectors, which are low dimensional representations of the input. In this paper, we propose a bottleneck feature supervised (BS) U-Net model and apply it to liver and tumor segmentation. Our main contributions are: (1) we propose a variation of the original U-Net that has better performance with a smaller number of parameters; (2) we propose a bottleneck feature supervised (BS) U-Net that contains an encoding U-Net and a segmentation U-Net. The encoding U-Net is first trained as an auto-encoder to get encodings of the label maps, which are subsequently used as additional supervision to train the segmentation U-Net. Compared with most U-Net-based models in the literature that only use the pair information between images and label maps, BS U-Net additionally uses the information extracted from the label maps as supervision. The model is evaluated on the liver and tumor segmentation (LiTS) competition. 2D BS U-Net achieves dice per case (DPC) 96.1% for liver segmentation and 56.9% for tumor segmentation. This result is better than most state-of-the-art 2D UNet-based networks in both tasks. Furthermore, the idea of bottleneck feature supervision can also be generalized to other U-Net-based models, making it have good potential for future development.
- Bottleneck, CNN, Encoding, Liver tumor, Segmentation, U-Net
Expert Systems with Applications, Vol. 145, 113131, 01.05.2020.
Research output: Journal Publications and Reviews (RGC: 21, 22, 62) › 21_Publication in refereed journal