Introduction to Cancer Segmentation

What is/are Cancer Segmentation?

Cancer Segmentation - Our model outperforms baseline models in 3D lung cancer segmentation experiments. ^[1] Automated pancreatic cancer segmentation is highly crucial for computer-assisted diagnosis. ^[2] OBJECTIVE In order to improve the efficiency of gastric cancer pathological slice image recognition and segmentation of cancerous regions, this paper proposes an automatic gastric cancer segmentation model based on Deeplab v3+ neural network. ^[3] Our experiments were performed on three publicly available whole-slide images of recent challenges (PAIP 2019: hepatocellular carcinoma segmentation; BACH 2020: breast cancer segmentation; CAMELYON 2016: metastasis detection in lymph nodes). ^[4] Besides, Optimal Kapur's based Multilevel Thresholding with Shell Game Optimization (SGO) algorithm (OKMT-SGO) is applied for breast cancer segmentation. ^[5] This paper first proposes an ensemble preprocessing method to improve the performance of a CNN for cervical cancer segmentation. ^[6] Several researchers have worked on breast cancer segmentation and classification using various imaging modalities. ^[7] Considering 3D information utilization and small sample sizes, we propose a model-driven deep learning method for pancreatic cancer segmentation based on spiral transformation. ^[8] In this paper we proposed a relative analysis of automatic lung cancer segmentation techniques. ^[9] CONCLUSIONS Our results demonstrate feasibility of the introduced CMEDL approach to produce reasonably accurate lung cancer segmentation from CBCT images. ^[10] The PAIP Liver Cancer Segmentation Challenge, organized in conjunction with the Medical Image Computing and Computer Assisted Intervention Society (MICCAI 2019), is the first image analysis challenge to apply PAIP datasets. ^[11] However, complex imaging background, highly characteristics variation and poor contrast hindered the research progress of the automatic rectal cancer segmentation. ^[12] However, complex imaging background, highly characteristics variation and poor contrast hindered the research progress of the automatic rectal cancer segmentation. ^[13] Inspired by this, we provide an in-depth look at bladder cancer segmentation using deep learning models. ^[14] The cost of data acquisition and annotation is relatively high, especially for skin cancer segmentation tasks. ^[15] This paper addresses the problem of liver cancer segmentation in Whole Slide Images (WSIs). ^[16] Purpose: The purpose of this study was to develop and evaluate lung cancer segmentation with a pretrained model and transfer learning. ^[17] The results showed that 3D V-Net architecture could conduct reliable rectal cancer segmentation on T2WI and DWI images. ^[18] This paper reviews this challenge and summarizes the top 10 submitted methods for lung cancer segmentation. ^[19] In this paper, we propose a deep supervised multi-scale network (DSM-Network), which achieves satisfied skin cancer segmentation result by utilizing the side-output layers of the network to aggregate information from shallow&deep layers, and designing a multi-scale connection block to handle a variety of cancer sizes’ changes. ^[20] METHODS In this work, we present a novel evolutionary framework for image enhancement, automatic global thresholding, and segmentation, which is here applied to different clinical scenarios involving bimodal MR image analysis: (i) uterine fibroid segmentation in MR guided Focused Ultrasound Surgery, and (ii) brain metastatic cancer segmentation in neuro-radiosurgery therapy. ^[21] The proposed models are tested on three benchmark datasets, such as blood vessel segmentation in retinal images, skin cancer segmentation, and lung lesion segmentation. ^[22] In comparison to research being conducted on cancers like prostate and breast, the literature for colorectal cancer segmentation is scarce. ^[23] In this paper, we propose a Bi-attention adversarial network for the prostate cancer segmentation automatically. ^[24] Automatic gastric cancer segmentation is a challenging problem in digital pathology image analysis. ^[25] We evaluate our model on benchmark segmentation datasets in skin cancer segmentation and lung lesion segmentation. ^[26] This paper, to improve the Lung cancer segmentation and classification a new model is introduce. ^[27] Accurate cancer segmentation helps doctors understand the location and size of cancer and make better diagnostic decisions. ^[28] Furthermore, to leverage the advantages of CNN and FCN, we integrate the two methods to obtain a complete framework for lung cancer segmentation. ^[29]

Lung Cancer Segmentation

Our model outperforms baseline models in 3D lung cancer segmentation experiments. ^[1] In this paper we proposed a relative analysis of automatic lung cancer segmentation techniques. ^[2] CONCLUSIONS Our results demonstrate feasibility of the introduced CMEDL approach to produce reasonably accurate lung cancer segmentation from CBCT images. ^[3] Purpose: The purpose of this study was to develop and evaluate lung cancer segmentation with a pretrained model and transfer learning. ^[4] This paper reviews this challenge and summarizes the top 10 submitted methods for lung cancer segmentation. ^[5] This paper, to improve the Lung cancer segmentation and classification a new model is introduce. ^[6] Furthermore, to leverage the advantages of CNN and FCN, we integrate the two methods to obtain a complete framework for lung cancer segmentation. ^[7]

Skin Cancer Segmentation

The cost of data acquisition and annotation is relatively high, especially for skin cancer segmentation tasks. ^[1] In this paper, we propose a deep supervised multi-scale network (DSM-Network), which achieves satisfied skin cancer segmentation result by utilizing the side-output layers of the network to aggregate information from shallow&deep layers, and designing a multi-scale connection block to handle a variety of cancer sizes’ changes. ^[2] The proposed models are tested on three benchmark datasets, such as blood vessel segmentation in retinal images, skin cancer segmentation, and lung lesion segmentation. ^[3] We evaluate our model on benchmark segmentation datasets in skin cancer segmentation and lung lesion segmentation. ^[4]

Breast Cancer Segmentation

Our experiments were performed on three publicly available whole-slide images of recent challenges (PAIP 2019: hepatocellular carcinoma segmentation; BACH 2020: breast cancer segmentation; CAMELYON 2016: metastasis detection in lymph nodes). ^[1] Besides, Optimal Kapur's based Multilevel Thresholding with Shell Game Optimization (SGO) algorithm (OKMT-SGO) is applied for breast cancer segmentation. ^[2] Several researchers have worked on breast cancer segmentation and classification using various imaging modalities. ^[3]

Rectal Cancer Segmentation

However, complex imaging background, highly characteristics variation and poor contrast hindered the research progress of the automatic rectal cancer segmentation. ^[1] However, complex imaging background, highly characteristics variation and poor contrast hindered the research progress of the automatic rectal cancer segmentation. ^[2] The results showed that 3D V-Net architecture could conduct reliable rectal cancer segmentation on T2WI and DWI images. ^[3]

Gastric Cancer Segmentation

OBJECTIVE In order to improve the efficiency of gastric cancer pathological slice image recognition and segmentation of cancerous regions, this paper proposes an automatic gastric cancer segmentation model based on Deeplab v3+ neural network. ^[1] Automatic gastric cancer segmentation is a challenging problem in digital pathology image analysis. ^[2]

Pancreatic Cancer Segmentation

Automated pancreatic cancer segmentation is highly crucial for computer-assisted diagnosis. ^[1] Considering 3D information utilization and small sample sizes, we propose a model-driven deep learning method for pancreatic cancer segmentation based on spiral transformation. ^[2]

Liver Cancer Segmentation

The PAIP Liver Cancer Segmentation Challenge, organized in conjunction with the Medical Image Computing and Computer Assisted Intervention Society (MICCAI 2019), is the first image analysis challenge to apply PAIP datasets. ^[1] This paper addresses the problem of liver cancer segmentation in Whole Slide Images (WSIs). ^[2]