Modified U-Net for fully automatic liver segmentation from abdominal CT-image

Mourya, Gajendra Kumar; Paul, Sudip; Handique, Akash; Baid, Ujjwal; Dutande, Prasad; Talbar, Sanjay Nilkant

doi:10.1504/ijbet.2022.125099

Cited by 3 publications

(1 citation statement)

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“…analysis. Because of this, we chose to conduct our experiment using the liver image segmentation dataset [16] and the MIT scene analysis dataset [17].…”

Section: Transfer Learning Mechanismmentioning

confidence: 99%

A Novel Approach to Optimizing Convolutional Neural Networks for Improved Digital Image Segmentation

Xing,

Ku,

Zhao

2024

International Journal of Intelligent Systems

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To divide a digital image into individual parts that share similar characteristics is known as digital image segmentation, and it is a vital research subject in the field of computer vision. Object recognition, medical imaging, surveillance, and video processing are just a few of the many real-world contexts where this study could prove useful. While digital image segmentation research has come a long way, there are still certain obstacles to overcome. Segmentation algorithms frequently encounter challenges in achieving both accuracy and efficiency when confronted with intricate settings, noisy pictures, or fluctuating lighting conditions. The absence of established evaluation standards adds complexity to the process of performing equitable comparisons among different segmentation methodologies. Due to the subjective nature of photo segmentation, attaining consistent results among specialists can be challenging. The integration of machine learning and deep neural networks into segmentation algorithms has introduced new challenges, including the need for large amounts of annotated data and the interpretability of the outcomes. Given these challenges, the objective of this study is to enhance the segmentation model. To this end, this research suggests a model of convolutional neural networks that is optimal for digital picture segmentation. The model is based on a dense convolution neural network, and it incorporates a transfer learning technique to significantly boost the model’s robustness and the quality of picture segmentation. The model’s adaptability to new datasets is improved by the incorporation of a transfer learning method. As demonstrated by experimental results on two publicly available datasets, the suggested methodology considerably enhances the resilience of digital picture segmentation.

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“…analysis. Because of this, we chose to conduct our experiment using the liver image segmentation dataset [16] and the MIT scene analysis dataset [17].…”

Section: Transfer Learning Mechanismmentioning

confidence: 99%

A Novel Approach to Optimizing Convolutional Neural Networks for Improved Digital Image Segmentation

Xing,

Ku,

Zhao

2024

International Journal of Intelligent Systems

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Literature survey on deep learning methods for liver segmentation from CT images: a comprehensive review

S. S.,

R. S.

2024

Multimed Tools Appl

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Fully Automatic Liver and Tumor Segmentation from CT Image Using an AIM-Unet

et al. 2023

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The segmentation of the liver is a difficult process due to the changes in shape, border, and density that occur in each section in computed tomography (CT) images. In this study, the Adding Inception Module-Unet (AIM-Unet) model, which is a hybridization of convolutional neural networks-based Unet and Inception models, is proposed for computer-assisted automatic segmentation of the liver and liver tumors from CT scans of the abdomen. Experimental studies were carried out on four different liver CT image datasets, one of which was prepared for this study and three of which were open (CHAOS, LIST, and 3DIRCADb). The results obtained using the proposed method and the segmentation results marked by the specialist were compared with the Dice similarity coefficient (DSC), Jaccard similarity coefficient (JSC), and accuracy (ACC) measurement parameters. In this study, we obtained the best DSC, JSC, and ACC liver segmentation performance metrics on the CHAOS dataset as 97.86%, 96.10%, and 99.75%, respectively, of the AIM-Unet model we propose, which is trained separately on three datasets (LiST, CHAOS, and our dataset) containing liver images. Additionally, 75.6% and 65.5% of the DSC tumor segmentation metrics were calculated on the proposed model LiST and 3DIRCADb datasets, respectively. In addition, the segmentation success results on the datasets with the AIM-Unet model were compared with the previous studies. With these results, it has been seen that the method proposed in this study can be used as an auxiliary tool in the decision-making processes of physicians for liver segmentation and detection of liver tumors. This study is useful for medical images, and the developed model can be easily developed for applications in different organs and other medical fields.

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Modified U-Net for fully automatic liver segmentation from abdominal CT-image

Cited by 3 publications

References 0 publications

A Novel Approach to Optimizing Convolutional Neural Networks for Improved Digital Image Segmentation

A Novel Approach to Optimizing Convolutional Neural Networks for Improved Digital Image Segmentation

Literature survey on deep learning methods for liver segmentation from CT images: a comprehensive review

Fully Automatic Liver and Tumor Segmentation from CT Image Using an AIM-Unet

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