Correction Learning for Medical Image Segmentation

Zhang, Guang; Dong, Shuhao; Xu, Hui; Zhang, Hongyu; Wu, Yongjian; Zhang, Yanwei; Xi, Xiaoming; Yin, Yilong

doi:10.1109/access.2019.2944849

Cited by 3 publications

(2 citation statements)

References 33 publications

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“…where t is any pixel located in the region of interest, namely T. C O is also calculated like Equation (15). High CD represent improvement of contrast on images.…”

Section: Quantitative Evaluationmentioning

confidence: 99%

“…Currently, most prior arts focus on providing a segmentation algorithm using deep learning solution for CT images [12,14]. The work presented in Reference [15] proposed a correction learning scheme that processes the segmented lesion on a cropped mammography from superpixel-based technique to be improved using block-based boundary correction. Despite the simplicity of Reference [15], erroneous segmentation does not modify any network's weight, while it also may not applicable for SXRs with many arduous textures.…”

Section: Introductionmentioning

confidence: 99%

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Transferable Architecture for Segmenting Maxillary Sinuses on Texture-Enhanced Occipitomental View Radiographs

et al. 2020

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Maxillary sinuses are the most prevalent locations for paranasal infections on both children and adults. Common diagnostic material for this particular disease is through the screening of occipitomental-view skull radiography (SXR). With the growing cases on paranasal infections, expediting the diagnosis has become an important innovation aspect that could be addressed through the development of a computer-aided diagnosis system. As the preliminary stage of the development, an automatic segmentation over the maxillary sinuses is required to be developed. This study presents a computer-aided detection (CAD) module that segments maxillary sinuses from a plain SXR that has been preprocessed through the novel texture-based morphological analysis (ToMA). Later, the network model from the Transferable Fully Convolutional Network (T-FCN) performs pixel-wise segmentation of the maxillary sinuses. T-FCN is designed to be trained with multiple learning stages, which enables re-utilization of network weights to be adjusted based on newer dataset. According to the experiments, the proposed system achieved segmentation accuracy at 85.70%, with 50% faster learning time.

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“…where t is any pixel located in the region of interest, namely T. C O is also calculated like Equation (15). High CD represent improvement of contrast on images.…”

Section: Quantitative Evaluationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Transferable Architecture for Segmenting Maxillary Sinuses on Texture-Enhanced Occipitomental View Radiographs

et al. 2020

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Supervised and Semi-supervised Methods for Abdominal Organ Segmentation: A Review

Senkyire

Liu

2021

Int. J. Autom. Comput.

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Hybrid Segmentation Algorithm for Medical Image Segmentation Based on Generating Adversarial Networks, Mutual Information and Multi-Scale Information

et al. 2020

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This paper proposes 3D-MedGAN, MLU-Net and Info-Max-Net models for overcoming the lack of labeled data and extracting the multi-level feature of images in medical image segmentation. 3D-MedGAN is aimed at dealing with the lack of labeled data in medical images. It uses a generative adversarial network to simulate data and then draws newly generated samples from the distribution learned by the model. Training the segmentation model by mixing generated samples with real samples can effectively improve the effect of the segmentation model. MLU-Net uses multiple layers of different levels of convolutional angles to extract feature information from multiple angles in medical images. By adopting the attention mechanism to fuse the multi-level feature information, MLU-Net is able to improve the feature expressions and segmentation effect. Info-Max-Net is aimed at handling the noise problem in medical images. When the information in the images is complex, it is difficult to extract features. Using mutual information to measure the dependency between the image and the extracted features can effectively reduce noise in the image and improve the effect of segmentation. At the same time, for solving the problem that the high dimension of the image makes it difficult to measure mutual information, this paper uses a lower bound BL-estimator to measure the mutual information between the optimized image and the extracted features. Therefore, the model can maintain a high convergence speed as it approaches the true value of mutual information. Considering that the quality of images generated by 3D-MedGAN are not as good as the original images, we combine the 3D-MedGAN, MLU-Net, and Info-Max-Net to improve the sensitivity and the power of feature extraction of the hybird model. The effectiveness of our model is verified through experiments of the 3D-MedGAN, MLU-Net, Info-Max-Net, and the hybrid model over the LIVER100 dataset.

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Correction Learning for Medical Image Segmentation

Cited by 3 publications

References 33 publications

Transferable Architecture for Segmenting Maxillary Sinuses on Texture-Enhanced Occipitomental View Radiographs

Transferable Architecture for Segmenting Maxillary Sinuses on Texture-Enhanced Occipitomental View Radiographs

Supervised and Semi-supervised Methods for Abdominal Organ Segmentation: A Review

Hybrid Segmentation Algorithm for Medical Image Segmentation Based on Generating Adversarial Networks, Mutual Information and Multi-Scale Information

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