Efficient liver segmentation with 3D CNN using computed tomography scans

• URL: http://arxiv.org/abs/2208.13271v1
• Date: Sun, 28 Aug 2022 19:02:39 GMT
• Title: Efficient liver segmentation with 3D CNN using computed tomography scans
• Authors: Khaled Humady, Yasmeen Al-Saeed, Nabila Eladawi, Ahmed Elgarayhi, Mohammed Elmogy, Mohammed Sallah
• Abstract summary: Liver diseases due to liver tumors are one of the most common reasons around the globe. Many imaging modalities can be used as aiding tools to detect liver tumors. This paper proposed an efficient automatic liver segmentation framework to detect and segment the liver out of CT abdomen scans.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The liver is one of the most critical metabolic organs in vertebrates due to its vital functions in the human body, such as detoxification of the blood from waste products and medications. Liver diseases due to liver tumors are one of the most common mortality reasons around the globe. Hence, detecting liver tumors in the early stages of tumor development is highly required as a critical part of medical treatment. Many imaging modalities can be used as aiding tools to detect liver tumors. Computed tomography (CT) is the most used imaging modality for soft tissue organs such as the liver. This is because it is an invasive modality that can be captured relatively quickly. This paper proposed an efficient automatic liver segmentation framework to detect and segment the liver out of CT abdomen scans using the 3D CNN DeepMedic network model. Segmenting the liver region accurately and then using the segmented liver region as input to tumors segmentation method is adopted by many studies as it reduces the false rates resulted from segmenting abdomen organs as tumors. The proposed 3D CNN DeepMedic model has two pathways of input rather than one pathway, as in the original 3D CNN model. In this paper, the network was supplied with multiple abdomen CT versions, which helped improve the segmentation quality. The proposed model achieved 94.36%, 94.57%, 91.86%, and 93.14% for accuracy, sensitivity, specificity, and Dice similarity score, respectively. The experimental results indicate the applicability of the proposed method.

Related papers

• LiverUSRecon: Automatic 3D Reconstruction and Volumetry of the Liver with a Few Partial Ultrasound Scans [1.2009760098637572]
  Liver volumetry using ultrasound (US) scans is challenging due to the inherent noisiness in US scans, blurry boundaries, and partial liver visibility. We address these challenges by using the segmentation masks of a few incomplete sagittal-plane US scans of the liver in conjunction with a statistical shape model (SSM) built using a set of CT scans of the liver. The resulting 3D liver reconstruction is accurate and leads to automatic liver volume calculation.
  arXiv  Detail & Related papers  (2024-06-27T17:10:10Z)
• Weakly-Supervised Detection of Bone Lesions in CT [48.34559062736031]
  The skeletal region is one of the common sites of metastatic spread of cancer in the breast and prostate. We developed a pipeline to detect bone lesions in CT volumes via a proxy segmentation task. Our method detected bone lesions in CT with a precision of 96.7% and recall of 47.3% despite the use of incomplete and partial training data.
  arXiv  Detail & Related papers  (2024-01-31T21:05:34Z)
• A Cascaded Approach for ultraly High Performance Lesion Detection and False Positive Removal in Liver CT Scans [15.352636778576171]
  Liver cancer has high morbidity and mortality rates in the world. Automatically detecting and classifying liver lesions in CT images have the potential to improve the clinical workflow. In this work, we customize a multi-object labeling tool for multi-phase CT images.
  arXiv  Detail & Related papers  (2023-06-28T09:11:34Z)
• Towards Simultaneous Segmentation of Liver Tumors and Intrahepatic Vessels via Cross-attention Mechanism [3.291044793301153]
  We propose a 3D U-shaped Cross-Attention Network (UCA-Net) to accurately segment 3D medical images. The UCA-Net uses a channel-wise cross-attention module to reduce the semantic gap between encoder and decoder and a slice-wise cross-attention module to enhance the contextual semantic learning ability. Experimental results show that the proposed UCA-Net can accurately segment 3D medical images and achieve state-of-the-art performance on the liver tumor and intrahepatic vessel segmentation task.
  arXiv  Detail & Related papers  (2023-02-20T06:17:03Z)
• CoRe: An Automated Pipeline for The Prediction of Liver Resection Complexity from Preoperative CT Scans [53.561797148529664]
  Tumors located in critical positions are known to complexify liver resections. CoRe is an automated medical image processing pipeline for the prediction of postoperative LR complexity.
  arXiv  Detail & Related papers  (2022-10-15T15:29:24Z)
• A unified 3D framework for Organs at Risk Localization and Segmentation for Radiation Therapy Planning [56.52933974838905]
  Current medical workflow requires manual delineation of organs-at-risk (OAR) In this work, we aim to introduce a unified 3D pipeline for OAR localization-segmentation. Our proposed framework fully enables the exploitation of 3D context information inherent in medical imaging.
  arXiv  Detail & Related papers  (2022-03-01T17:08:41Z)
• Exploiting full Resolution Feature Context for Liver Tumor and Vessel Segmentation via Fusion Encoder: Application to Liver Tumor and Vessel 3D reconstruction [19.048952361722876]
  We introduce a multi-scale feature context fusion network called TransFusionNet based on Transformer and SEBottleNet. Experiments show that TransFusionNet is better than the state-of-the-art method on both the public dataset LITS and 3Dircadb and our clinical dataset. We also propose an automatic 3D reconstruction algorithm based on the trained model.
  arXiv  Detail & Related papers  (2021-11-26T02:48:48Z)
• Explainable multiple abnormality classification of chest CT volumes with AxialNet and HiResCAM [89.2175350956813]
  We introduce the challenging new task of explainable multiple abnormality classification in volumetric medical images. We propose a multiple instance learning convolutional neural network, AxialNet, that allows identification of top slices for each abnormality. We then aim to improve the model's learning through a novel mask loss that leverages HiResCAM and 3D allowed regions.
  arXiv  Detail & Related papers  (2021-11-24T01:14:33Z)
• Realistic Ultrasound Image Synthesis for Improved Classification of Liver Disease [54.69792905238048]
  convolutional neural networks (CNNs) have been investigated for classifying liver disease from ultrasound (US) data. We propose a novel generative adversarial network (GAN) architecture for realistic diseased and healthy liver US image synthesis.
  arXiv  Detail & Related papers  (2021-07-27T12:37:19Z)
• Multi-Slice Low-Rank Tensor Decomposition Based Multi-Atlas Segmentation: Application to Automatic Pathological Liver CT Segmentation [4.262342157729123]
  Liver segmentation from abdominal CT images is an essential step for liver cancer computer-aided diagnosis and surgical planning. Currently, the accuracy and robustness of existing liver segmentation methods cannot meet the requirements of clinical applications. We propose a novel low-rank tensor decomposition (LRTD) based multi-atlas segmentation (MAS) framework that achieves accurate and robust pathological liver segmentation of CT images.
  arXiv  Detail & Related papers  (2021-02-24T04:09:39Z)
• A Global Benchmark of Algorithms for Segmenting Late Gadolinium-Enhanced Cardiac Magnetic Resonance Imaging [90.29017019187282]
  " 2018 Left Atrium Challenge" using 154 3D LGE-MRIs, currently the world's largest cardiac LGE-MRI dataset. Analyse of the submitted algorithms using technical and biological metrics was performed. Results show the top method achieved a dice score of 93.2% and a mean surface to a surface distance of 0.7 mm.
  arXiv  Detail & Related papers  (2020-04-26T08:49:17Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.