Deep Learning based Liver Segmentation: A Review

Napte, Kiran; Mahajan, Anurag

doi:10.18280/ria.360620

Cited by 2 publications

(2 citation statements)

References 27 publications

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“…HCC is the most prevalent liver cancer caused by liver tissue malignant cell development. Blood vessels form a hepatic hemangioma [2]. Secondary metastatic liver cancer occurs when cancer spreads from another bodily organ [3].…”

Section: Introductionmentioning

confidence: 99%

ESP-UNet: Encoder-Decoder Convolutional Neural Network with Edge-Enhanced Features for Liver Segmentation

Napte,

Mahajan,

Urooj

2023

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Precise liver segmentation in Computed Tomography (CT) scans plays a pivotal role in numerous biomedical applications, spanning surgical planning, postoperative assessment, and pathological detection of hepatic diseases. The task, however, is fraught with challenges due to the inherent complexities of liver morphology, including indistinct boundaries, irregular shapes, and complex architecture. Consequences of under-segmentation and oversegmentation of the liver in CT images can lead to inaccurate localizations and diagnoses of liver diseases, underscoring the necessity for accurate segmentation. This study introduces an Encoder-Decoder Convolutional Neural Network, termed ESP-UNet, which is designed to reduce under-segmentation and over-segmentation, thereby enhancing the accuracy of liver segmentation. The proposed ESP-UNet employs Kirsch's filter to bolster the texture and edge information of liver images, thus aiding in improved segmentation performance. The efficacy of the ESP-UNet segmentation technique was evaluated using the LiTS dataset, with performance metrics including accuracy, Dice Score Coefficient (DSC), Volume Overlapping Error (VOE), and Relative Volume Difference (RVD). The algorithm yielded impressive results, with a Dice Score of 0.959, a VOE of 0.089, a Jaccard Index (JI) of 0.921, and an RVD of 0.09. Despite requiring a larger number of trainable parameters and an increased network complexity due to the parallel UNet, the proposed ESP-UNet not only enhances liver segmentation but also has the potential to improve the detection of liver cancer at the image borders. A comparison with existing state-of-the-art liver segmentation techniques revealed that ESP-UNet offers superior performance, validating its potential as a useful tool in the diagnosis and treatment of liver diseases.

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Section: Introductionmentioning

confidence: 99%

ESP-UNet: Encoder-Decoder Convolutional Neural Network with Edge-Enhanced Features for Liver Segmentation

Napte,

Mahajan,

Urooj

2023

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“…The emergence of deep learning in recent decade has revolutionized the study of image processing and computer vision [11,12]. In this field, automatic depth estimation is now a research hot spot that has brought unprecedented opportunities for animation production.…”

Section: Introductionmentioning

confidence: 99%

Automatic Depth Estimation and Background Blurring of Animated Scenes Based on Deep Learning

He,

Jia

2023

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Animation technology enables more accurate depth estimation and background blurring of animated scenes as it can enhance the sense of reality of the vision and increase its depth, thus it has become a hot spot in relevant research and production these days. However, although deep learning has made significant progresses in many research fields, its application in depth estimation and background blurring of animated scenes is still facing a few challenges. Most available technologies are for real world images, not animations, so there are certain difficulties capturing the unique styles of animations and their details. This study proposes two technical schemes specifically designed for animated scenes: a depth estimation model based on DenseNet, and a deblurring algorithm based on Very Deep Super Resolution (VDSR), in the hopes of providing solutions for the above mentioned matters, as well as forging more efficient and accurate tools for the animation industry.

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Deep Learning based Liver Segmentation: A Review

Cited by 2 publications

References 27 publications

ESP-UNet: Encoder-Decoder Convolutional Neural Network with Edge-Enhanced Features for Liver Segmentation

ESP-UNet: Encoder-Decoder Convolutional Neural Network with Edge-Enhanced Features for Liver Segmentation

Automatic Depth Estimation and Background Blurring of Animated Scenes Based on Deep Learning

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