Ms. Rong Fan Profile

Rong Fan

at Waseda Univ

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Publications (2)

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Proceedings Article | 30 June 2021 Paper

Spatial information using CRF for brain tumor segmentation

Yawen Chen, Sei-ichiro Kamata, Rong Fan

Proceedings Volume 11878, 118780Q (2021) https://doi.org/10.1117/12.2599732

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In this work, we proposed a method combined the fuzzy spatial correlation of voxels in the MRI images obtained from a 3D network using CRF with the slice information captured by an ordinary 2D network to focus on the brain tumor segmentation task. Considering the expensive devices required by 3D networks while 2D networks can loss the information in the channel direction which leads to many false positive predictions, the proposed one can be a favorable direction to get more accurate features of the brain tumor. We take MRI images with 4 modalities in BRATS2018 dataset as the input of the 3D CNN after reducing the resolution. The CRF is used to calculate the neighboring correlation after the CNN feature extractor and can generate the probability map. The 2D network takes 2D slices in 4 modalities from the MRI images as input and output the segmentation map. The 2D segmentation maps are joining to 3D in order and combined with the probability map to get the final result. Compared with the state-of-the-art and the baseline method with the average Dice less than 0.85, the proposed is time and memory saving with the average Dice nearly 0.88.

Proceedings Article | 30 June 2021 Paper

Pulmonary nodule detection using improved faster R-CNN and 3D Resnet

Rong Fan, Sei-ichiro Kamata, Yawen Chen

Proceedings Volume 11878, 118780F (2021) https://doi.org/10.1117/12.2599884

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Pulmonary nodule detection system consists of two steps: candidate detection and false positive reduction. To dynamically adapt the sizes and ratios of the nodules, Local Density based Iterative Self-Organizing Data Analysis Techniques Algorithm (D-ISODATA) is proposed for automated anchor boxes configuration. For candidate detection, instead of fixed anchor, D-ISODATA is utilized for automatically generate anchors to adapt to high variability of nodules. D-ISODATA initializes clustering center and removes noises based on the principle of maximum local density and further clustering is carried out with self-adaptability. In addition, attention mechanism is adopted in feature channels to enable the model to focus on nodule-related features. For false positive reduction, 3D Resnet is utilized to extract the three-dimensional features of nodules. Experiments are carried out on LUNA16 dataset and show out a sensitivity of 93.6% with 0.15 false positive per scan. The results show preferable performance of the proposed method.

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