Abstract
Data imbalance is often encountered in deep learning process and is harmful to model training. The imbalance of hard and easy samples in training datasets often occurs in the segmentation tasks from Contrast Tomography (CT) scans. However, due to the strong similarity between adjacent slices in volumes and different segmentation tasks (the same slice may be classified as a hard sample in liver segmentation task, but an easy sample in the kidney or spleen segmentation task), it is hard to solve this imbalance of training dataset using traditional methods. In this work, we use a pre-training strategy to distinguish hard and easy samples, and then increase the proportion of hard slices in training dataset, which could mitigate imbalance of hard samples and easy samples in training dataset, and enhance the contribution of hard samples in training process. Our experiments on liver, kidney and spleen segmentation show that increasing the ratio of hard samples in the training dataset could enhance the prediction ability of model by improving its ability to deal with hard samples. The main contribution of this work is the application of pre-training strategy, which enables us to select training samples online according to different tasks and to ease data imbalance in the training dataset.
Highlights
Accurate segmentation of the liver can greatly help the subsequent segmentation of liver tumors, as well as assisting doctors in making accurate disease condition assessment and treatment planning of patients [1]
As for the strong similarity between adjacent slices in Contrast Tomography (CT) scans, we assume that the contribution of some slices could be replaced by others in the training process
These results suggest that there is redundancy in the training dataset, and that too little training data is harmful in the model training process
Summary
Accurate segmentation of the liver can greatly help the subsequent segmentation of liver tumors, as well as assisting doctors in making accurate disease condition assessment and treatment planning of patients [1]. Automatic segmentation tools are required for practical clinical applications Automatic segmentation methods such as region growing, intensity thresholding, and deformable model-based methods have achieved automatic or semi-automatic segmentation to a certain extent, with good segmentation results. Methods of deep learning, especially full convolutional networks (FCNs), have achieved great success on a broad array of recognition problems [2,3,4] Many researchers advance this stream using deep learning methods in segmentation tasks such as liver [1,5,6,7], kidney [8], vessel [9,10,11] and pancreas [12,13,14]. There often are two kinds of data imbalance problems in the training process for the
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