Deep Semantic Segmentation of Kidney and Space-occupying Lesion Area Using SCNN and ResNet models combined with SIFT-Flow Algorithm (Preprint)

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.

Deep Semantic Segmentation of Kidney and Space-occupying Lesion Area Using SCNN and ResNet models combined with SIFT-Flow Algorithm (Preprint)

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 which is divided into two stages: image retrieval and semantic segmentation. 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.

Fast depth extraction from a single image

Predicting depth from a single image is an important problem for understanding the 3-D geometry of a scene. Recently, the nonparametric depth sampling (DepthTransfer) has shown great potential in solving this problem, and its two key components are a Scale Invariant Feature Transform (SIFT) flow–based depth warping between the input image and its retrieved similar images and a pixel-wise depth fusion from all warped depth maps. In addition to the inherent heavy computational load in the SIFT flow computation even under a coarse-to-fine scheme, the fusion reliability is also low due to the low discriminativeness of pixel-wise description nature. This article aims at solving these two problems. First, a novel sparse SIFT flow algorithm is proposed to reduce the complexity from subquadratic to sublinear. Then, a reweighting technique is introduced where the variance of the SIFT flow descriptor is computed at every pixel and used for reweighting the data term in the conditional Markov random fields. Our proposed depth transfer method is tested on the Make3D Range Image Data and NYU Depth Dataset V2. It is shown that, with comparable depth estimation accuracy, our method is 2–3 times faster than the DepthTransfer.