Liver Tumor Segmentation Research Topics

Liver Tumor Segmentation (LTS) Research Topics is the process of identifying and defining the areas that are corresponding to the position by using the MRI or CT scans. This technology is now being widely utilized in many of the fields. It will predict the enhanced segmentation outcomes. Then in this we offer the details that are related to this proposed technique.

  1. Define Liver Tumor Segmentation using Deep Learning Techniques

Initially we begin with the explanation for this proposed technique. LTS using deep learning techniques contains the procedure of finding and describing the regions that are significant to the liver and tumors in medical images like MRI or CT scans. Deep Learning models, specifically Convolutional Neural Networks (CNNs), are trained on marked image data to automatically segment these structures. The process generally contains preprocessing, model training and post-pretraining procedures to improve the segmentation outcomes. Accurate segmentation is essential for tasks like disease monitoring, medical research and treatment planning.

  1. What is Liver Tumor Segmentation using Deep Learning Techniques?

There after the explanation we look for the brief explanation for this proposed LTS using DL technique. The deep learning technology is an advanced neural network framework to find and define the liver tissue and tumor areas over medical imaging like MRI or CT scans. By using deep learning, the segmentation tasks become more scalable and consistent, possibly enhancing patient outcomes. This procedure generates the traditionally physical task of overviewing these regions, improving accuracy and speed. Methods like U-Net or its variants are generally utilized, planned to find the difficult patterns in medical images. The technology intends to help clinicians and radiologists in analysis, monitoring the progression and treatment planning of liver-related diseases.

  1. Where Liver Tumor Segmentation using Deep Learning Techniques used?

After the brief explanation we understand where to use this proposed technique. LTS using deep learning is important in clinical settings for personalized treatment policies and early detection of liver defects. It is widely utilized in medical imaging investigation to support radiologists in planning surgeries, monitoring treatment processes and diagnosing liver diseases.

  1. Why Liver Tumor Segmentation using Deep Learning Techniques technology proposed? , previous technology issues

In this research we proposed the deep learning based LTS. It is proposed to tackle the issues in traditional and manual segmentation techniques that are subjective, prone to human error and time consuming. The previous technologies depend on hand-crafted features and need extensive expertise, causing irregularity and unpredictability among practitioners. Deep learning methods generate and regulate the procedure, more accurate, consistent findings and more accurate. In addition deep learning will adjust to different datasets, creating it an adaptable tool in clinical surroundings. This enhancement helps increase patient care by allowing treatment planning and more consistent analysis.

  1. Algorithms / protocols

Our proposed deep learning based LTS technique employs the following methods to overcome the existing technology issues. The methods that we utilized are UNet, Squeeze and Excitation (SE), Windowing, Res2Net, Encoder-Decoder Convolutional Neural Networks (EDCNN) and Cumulative Distributive Function.

  1. Comparative study / Analysis

Here we look over the methods that we proposed for this research to compare and to get the best findings. The methods that we compared are as follows:

  • Enhance the visibility of liver and tumor structures by the preprocessing methods with Cumulative Distribution Function (CDF) and windowing and it will decrease the effect of intensity changes and to enhance the concept of the method.
  • Generate and enhance “Encoder-Decoder Convolutional Neural Network” (EDCNN) technique by concentrating on liver segmentation to allow the findings of exact liver boundary.
  • Squeeze and Excitation (SE) networks and Res2Net bottleneck modules which will be added to the UNet design to improve the propagation and feature extraction, which will help in accurate tumor segmentation.
  1. Simulation results / Parameters

Let’s see our proposed research performance by comparing the parameters or performance metrics with the existing technologies and show that our research will give the best findings. The metrics that we compared are Relative Volume Difference (RVD), Dice score (DICE), Maximum Surface Distance (MSD), Volumetric Overlap Error (VOE) and Average Symmetric Surface Distance (ASSD).

  1. Dataset LINKS / Important URL

LTS using deep learning technology is proposed in this research and it addresses some existing technology problems to overcome that. The following are some important links that give assistance to us when any doubts arise between us.

  1. Liver Tumor Segmentation using Deep Learning Techniques Applications

The LTS using deep learning technology is important for monitoring disease progression, early identification and personalized treatment planning in liver-related healthcare situations. It is also utilized for this research is useful in medical imaging to support in analyzing liver diseases, obtaining treatment response and planning surgical inventions.

  1. Topology for Liver Tumor Segmentation using Deep Learning Techniques

Now we look for the topology that will be used for this proposed LTS using deep learning techniques. It frequently contains Convolutional Neural Networks (CNNs), specifically U-Net and its variations, planned to achieve pixel-wise segmentation of medical images. These networks utilize an encoder-decoder framework, where the encoder takes out the features and the decoder recreates segmentation masks, permitting for accurate description of liver and tumor areas.

  1. Environment for Liver Tumor Segmentation using Deep Learning Techniques

Environment for LTS utilizing the DL techniques generally contains a strong computational structure with increased performance GPUs, medical image datasets, and focused software structures like PyTorch and TensorFlow. This framework permits the clinicians and investigators to test, train and arrange deep learning models for accurate medical image examination.

  1. Simulation tools

Software requirements to be required for this research LTS using deep learning techniques are mentioned below. The developmental tool that was needed to simulate the work is Python – 3.11.4. Then the operating system that is employed to operate this work is Windows 10 (64-bit).

  1. Results

Here the LTS using deep learning technique is proposed in this research and it addresses several previous technology issues. In this research we compared various metrics or parameters to achieve that our proposed research provides the best accuracy. Then it is implemented by using developmental tool Python 3.11.4 to obtain the best findings.

Liver Tumor Segmentation Research Ideas:

The following are some of the research topics that are related to this proposed Liver and Tumor segmentation. These topics provide assistance to us, when we go through this research related concepts, techniques, applications or any other information that are relevant to this proposed technology.

  1. A Dual Channel Multiscale Convolution U-Net Methodfor Liver Tumor Segmentation from Abdomen CT Images
  2. Extended Res-UNet with Hierarchical Inner-Modules for Liver Tumor Segmentation from CT Volumes
  3. Liver and Liver Tumor Segmentation Using Modified Encoder Decoder Network and Find the Tumor Geometry
  4. SBCNet: Scale and Boundary Context Attention Dual-Branch Network for Liver Tumor Segmentation
  5. Automatic Liver Tumor Segmentation on Dynamic Contrast Enhanced MRI Using 4D Information: Deep Learning Model Based on 3D Convolution and Convolutional LSTM
  6. V-Net Based Liver and Tumor Segmentation Using 3D ConditionalRandom Fields and Graph Cuts Method
  7. TD-Net: A Hybrid End-to-End Network for Automatic Liver Tumor Segmentation From CT Images
  8. Liver and Tumor Segmentation in Selective Internal Radiation Therapy 99mTc-MAA SPECT/CT Images using MANet and Histogram Adjustment
  9. DARN: Deep Attentive Refinement Network for Liver Tumor Segmentation from 3D CT volume
  10. CC-DenseUNet: Densely Connected U-Net with Criss-Cross Attention for Liver and Tumor Segmentation in CT Volumes
  11. Densely Connected U-Net With Criss-Cross Attention for Automatic Liver Tumor Segmentation in CT Images
  12. Liver Tumor Segmentation From Computed Tomography Images Through Convolutional Neural Networks
  13. DHT-Net: Dynamic Hierarchical Transformer Network for Liver and Tumor Segmentation
  14. Machine Learning for Liver and Tumor Segmentation in Ultrasound Based on Labeled CT and MRI Images
  15. Deep Learning-Based Liver Tumor Segmentation: A Comparative Study of U-Net Variants for Medical Imaging Analysis
  16. ACF-TransUNet: Attention-based Coarse-Fine Transformer U-Net for Automatic Liver Tumor Segmentation in CT Images
  17. Bridging the Gap Between 2D and 3D Contexts in CT Volume for Liver and Tumor Segmentation
  18. Liver Tumor Segmentation using Hybrid Residual Network and Conditional Random Fields
  19. Efficient Brain and Liver Tumor Segmentation using Seagull Optimization Algorithm based Super Pixel Fuzzy Clustering
  20. DefED-Net: Deformable Encoder-Decoder Network for Liver and Liver Tumor Segmentation
  21. Learning From Synthetic CT Images via Test-Time Training for Liver Tumor Segmentation
  22. 5 Automatic Liver Tumor Segmentation from Computed Tomography Images Based on 2D and 3D Deep Neural Networks
  23. Automatic Detection and Segmentation of Liver Tumors in Multi- phase CT Images by Phase Attention Mask R-CNN
  24. Towards Simultaneous Segmentation Of Liver Tumors And Intrahepatic Vessels Via Cross-Attention Mechanism
  25. Design and Implementation of an Optimized Mask RCNN Model for Liver Tumour Prediction and Segmentation
  26. Semantic Segmentation of Liver Tumor in Contrast-enhanced Hepatic CT by Using Deep Learning with Hessian-based Enhancer with Small Training Dataset Size
  27. Advanced Learning-Based Segmentation of Liver and Tumor 3D Images for Early Disease Diagnosis
  28. TransFusionNet: Semantic and Spatial Features Fusion Framework for Liver Tumor and Vessel Segmentation Under JetsonTX2
  29. Automatic Segmentation and Categorization of Liver Tumors in CT Scans: A Bibliometric Analysis
  30. PA-Net: A phase attention network fusing venous and arterial phase features of CT images for liver tumor segmentation
  31. ELTS-Net: An enhanced liver tumor segmentation network with augmented receptive field and global contextual information
  32. Tumor conspicuity enhancement-based segmentation model for liver tumor segmentation and RECIST diameter measurement in non-contrast CT images
  33. DA-Tran: Multiphase liver tumor segmentation with a domain-adaptive transformer network
  34. S2DA-Net: Spatial and spectral-learning double-branch aggregation network for liver tumor segmentation in CT images
  35. RMAU-Net: Residual Multi-Scale Attention U-Net For liver and tumor segmentation in CT images
  36. A three-path network with multi-scale selective feature fusion, edge-inspiring and edge-guiding for liver tumor segmentation
  37. Leverage prior texture information in deep learning-based liver tumor segmentation: A plug-and-play Texture-Based Auto Pseudo Label module
  38. Efficient two-step liver and tumour segmentation on abdominal CT via deep learning and a conditional random field
  39. MDCF_Net: A Multi-dimensional hybrid network for liver and tumor segmentation from CT
  40. Automatic liver tumor segmentation from CT images using hierarchical iterative superpixels and local statistical features
  41. HFRU-Net: High-Level Feature Fusion and Recalibration UNet for Automatic Liver and Tumor Segmentation in CT Images
  42. MS-UNet: A multi-scale UNet with feature recalibration approach for automatic liver and tumor segmentation in CT images
  43. Segmentation of liver tumors in multiphase computed tomography images using hybrid method
  44. CAAGP: Rethinking channel attention with adaptive global pooling for liver tumor segmentation
  45. X-Net: Multi-branch UNet-like network for liver and tumor segmentation from 3D abdominal CT scans
  46. A deep attention network via high-resolution representation for liver and liver tumor segmentation
  47. Weakly-Supervised teacher-Student network for liver tumor segmentation from non-enhanced images
  48. United adversarial learning for liver tumor segmentation and detection of multi-modality non-contrast MRI
  49. An Across Feature Map Attention-Based Deep Learning Method for Small Liver Tumor Segmentation in CT Scans
  50. Deep learning-based automated segmentation of liver and tumors on CT arterial portography and hepatic arteriography for Y-90 radioembolization