Previously submitted to: JMIR mHealth and uHealth (no longer under consideration since Aug 30, 2018)
Date Submitted: Aug 29, 2018
Open Peer Review Period: Aug 29, 2018 - Aug 30, 2018
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Deep Semantic Segmentation of Kidney and Space-occupying Lesion Area Using SCNN and ResNet models combined with SIFT-Flow Algorithm
Background:
Renal segmentation is one of the most fundamental and challenging tasks in computer aided diagnosis systems.
Objective:
In order to overcome the shortcomings of automatic kidney segmentation based on deep network for abdominal CT images, a two-stage semantic segmentation of kidney and space-occupying lesion area based on SCNN and ResNet models combined with SIFT-flow transformation is proposed in paper,
Methods:
To facilitate the image retrieval, a Metric Learning-based approach is firstly proposed to construct a deep convolutional neural network structure using SCNN and ResNet network to extract image features and minimize the impact of interference factors on features, so as to obtain the ability to represent the abdominal CT scan image with the same angle under different imaging conditions. And then, SIFT Flow transformation is introduced, which adopts MRF to fuse label information, priori spatial information and smoothing information to establish the dense matching relationship of pixels so that the semantics can be transferred from the known image to the target image to obtain the semantic segmentation result of kidney and space-occupying lesion area.
Results:
In order to validate effectiveness and efficiency of our proposed method, we conduct experiments on self-establish CT dataset, focus on kidney organ and most of which have tumors inside of the kidney, and abnormal deformed shape of kidney. The experimental results qualitatively and quantitatively show that the accuracy of kidney segmentation is greatly improved, and the key information of the proportioned tumor occupying a small area of the image are exhibited a good segmentation results.
Conclusions:
The proposed segmentation algorithm can be effectively applied in clinical diagnosis, help doctors to assist diagnosis, greatly improve the efficiency of work, less error probability.
Citation
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