Fine-grained tumor segmentation on computed tomography slices by leveraging bottom-up and top-down strategies

Abstract

Fully Convolutional Neural Networks (FCNNs) have been widely employed to solve object segmentation tasks effectively in both the computer vision and medical image processing fields in recent years. In object segmentation, FCNNs play a pixel-level prediction role in generating segmented predictions pixel-by-pixel, but they ignore the relationships among generated pixels on the output image. Moreover, blurry boundaries of predicted objects are another common obstacle in this task, because FCNNs usually generate low-frequency components of an image well, but they lack clear high-frequency information inside. In order to solve these problems, we introduce a top-down strategy by globally considering object shapes and context information. Moreover, based on original pixel-wise loss functions which we call a bottom-up strategy, we formulate the tumor segmentation task as a regression problem by using Jaccard Similarity Coefficient (JSC) which is usually one of the main metrics for evaluating the performance of segmentation methods. We directly propose a JSC loss to further optimize network parameters for globally evaluating the whole outputs of tumors. Furthermore, the new loss also alleviates the effect of severe class imbalanced problems between tumor regions and non-tumor regions when training FCNNs. By leveraging the bottom-up and top-down strategies together, our model can obtain more fine-grained tumor segmentation results and also be easily embedded into any FCNN framework for other object segmentation tasks. Detailed experimental results on common metrics demonstrate the superior performance of our proposed method for Kidney Tumor Segmentation Challenge 2019 among more than 100 involved teams.