Three-dimensional (3D) liver tumor segmentation from Computed Tomography (CT) images is a prerequisite for computer-aided diagnosis, treatment planning, and monitoring of liver cancer. Despite many years of research, 3D liver tumor segmentation remains a challenging task. In this paper, we propose an effective and efficient method for tumor segmentation in liver CT images using encoder-decoder based octave convolution networks. Compared with other convolution networks utilizing standard convolution for feature extraction, the proposed method utilizes octave convolutions for learning multiple-spatial-frequency features, thus can better capture tumors with varying sizes and shapes. The proposed network takes advantage of a fully convolutional architecture which performs efficient end-to-end learning and inference. More importantly, we introduce a deep supervision mechanism during the learning process to combat potential optimization difficulties, and thus the model can acquire a much faster convergence rate and more powerful discrimination capability. Finally, we integrate octave convolutions into the encoder-decoder architecture of UNet, which can generate high resolution tumor segmentation in one single forward feeding without post-processing steps. Both architectures are trained on a subset of the LiTS (Liver Tumor Segmentation) Challenge. The proposed approach is shown to significantly outperform other networks in terms of various accuracy measures and processing speed.
Automatic segmentation of brain tumors from multi-modalities magnetic resonance image data has the potential to enable preoperative planning and intraoperative volume measurement. Recent advances in deep convolutional neural network technology have opened up an opportunity to achieve end-to-end segmenting the brain tumor areas. However, the medical image data used in brain tumor segmentation are relatively scarce and the appearance of brain tumors is varied, so that it is difficult to find a learnable pattern to directly describe tumor regions. In this paper, we propose a novel cross-modalities interactive feature learning framework to segment brain tumors from the multi-modalities data. The core idea is that the multi-modality MR data contain rich patterns of the normal brain regions, which can be easily captured and can be potentially used to detect the non-normal brain regions, i.e., brain tumor regions. The proposed multi-modalities interactive feature learning framework consists of two modules: cross-modality feature extracting module and attention guided feature fusing module, which aim at exploring the rich patterns cross multi-modalities and guiding the interacting and the fusing process for the rich features from different modalities. Comprehensive experiments are conducted on the BraTS 2018 benchmark, which show that the proposed cross-modality feature learning framework can effectively improve the brain tumor segmentation performance when compared with the baseline methods and state-of-the-art methods.
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