Compressed sensing (CS) image reconstruction in CT suffers from the drawbacks such as 1) appearance of staircase artifacts and 2) loss in image textures and smooth intensity changes. These drawbacks stem from the fact that CS is based on approximating the image by a piecewise-constant function. To overcome this drawback, we have already proposed a framework to improve image quality in CS using deep learning. In this framework, FBP reconstructed image and CS (TV or Nonlocal TV) reconstructed image are inputted to CNN with two input channels and single output channel, and a final reconstructed image is obtained by the output of CNN. Parameters (weight and bias) of CNN together with a regularization parameter of CS are estimated by minimizing an average least-squares loss function by using learning data, i.e. a set of triplet of degraded FBP reconstruction, CS reconstruction, and answer image. In this paper, this framework is extended to 3-D image reconstruction in helical cone-beam CT operated with lowdose scanning protocol. Parameters (weight and bias) of CNN together with a regularization parameter of CS are estimated by minimizing an average least-squares loss function by using learning data, i.e. a set of triplet of degraded FBP reconstruction, CS reconstruction, and answer image. In this paper, this framework was extended to 3-D image reconstruction in helical cone-beam CT operated with lowdose scanning protocol. The extension was done in the following way. First, we prepare N different 2-D denoising CNN (CNN1, CNN2, . . . , CNNN ) dependent on the slice position n. Each slice of the short-scan FDK reconstruction without denoising yi and with 3-D TV (or Nonlocal TV) denoising zi are inputted to CNNn with the closest slice index n, which yields a corresponding output image for each slice xi . The final reconstructed image is obtained by stacking every slice xi (i = 1, 2, . . . , I).
Swallowing is achieved by a sequence of actions performed by cervical structures. Although a lot of patients suffer from dysphagia in the world, the mechanism and kinematics of swallowing are not elucidated sufficiently. This study aims to segment intervertebral disks (IDs), which are ones of representative cervical structures, in videofluorographic (VF) images by use of convolutional neural network (CNN). The proposed method consists of three steps: extraction of cervical masks, CNN-based segmentation of candidate regions of IDs, and the elimination of false positives. This segmentation method was applied to actual VF images of eleven participants that have fifty-one not-occluded IDs, and forty-three IDs were segmented successfully.
This study proposes an interactive image segmentation method based on high dimensional self organizing maps (SOMs). The proposed method was applied to gray-scale and color images. The experimental results demonstrated that higher dimensional SOMs were able to achieve more accurate segmentation.
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