Query: colorectal cancer segmentation

title: Colorectal Segmentation Using Multiple Encoder Decoder Network in Colonoscopy Images
abstract: Colorectal cancer is the third most common cancer which causes of cancer related deaths. Therefore, early diagnosis of polyps by colonoscopy could result in successful treatment. Diagnosis of polyps in colonoscopy videos is a challenging task due to variations in the size and shape of polyps. In this paper, we propose a polyp segmentation method based on the encoder decoder network. Performance of the method is enhanced by two strategies, we perform a novel database augmentation method for colonoscopy images in the training phase. Besides, in the test phase, we perform an effective prediction by combining multi model to compare the probability of each image that is produced by the network. Evaluation of the proposed method using the ETIS LariPolypDB database shows that our proposed method outperforms state of the art results.

title: Multi Texture Analysis of Colorectal Cancer Continuum Using Multispectral Imagery
abstract: This paper proposes to characterize the continuum of colorectal cancer (CRC) using multiple texture features extracted from multispectral optical microscopy images. Three types of pathological tissues (PT) are considered: benign hyperplasia, intraepithelial neoplasia and carcinoma.  In the proposed approach, the region of interest containing PT is first extracted from multispectral images using active contour segmentation. This region is then encoded using texture features based on the Laplacian of Gaussian (LoG) filter, discrete wavelets (DW) and gray level co occurrence matrices (GLCM) To assess the significance of textural differences between PT types, a statistical analysis based on the Kruskal Wallis test is performed. The usefulness of texture features is then evaluated quantitatively in terms of their ability to predict PT types using various classifier models.  Preliminary results show significant texture differences between PT types, for all texture features (p value 0.01) Individually, GLCM texture features outperform LoG and DW features in terms of PT type prediction. However, a higher performance can be achieved by combining all texture features, resulting in a mean classification accuracy of 98.92% sensitivity of 98.12% and specificity of 99.67%  These results demonstrate the efficiency and effectiveness of combining multiple texture features for characterizing the continuum of CRC and discriminating between pathological tissues in multispectral images.

title: Ensemble of Instance Segmentation Models for Polyp Segmentation in Colonoscopy Images
abstract: Colorectal cancer is the second most frequently diagnosed cancer in women and the third most frequently diagnosed cancer in men. At least 80% 95% of the colorectal cancers are evolved from intestinal polyps. Although colonoscopy is regarded as the most effective method for screening and diagnosis, the success of the procedure is highly dependent on the level of hand eye coordination and the operator skills. Thus, we are primarily motivated by the need for obtaining an early and accurate diagnosis of polyps in the colonoscopy images. In this paper, we employed the powerful object detection neural network "Mask R CNN" to identify and segment polyps in the colonoscopy images. Also, we proposed an ensemble method to combine the two Mask R CNN models with different backbone structures (ResNet50 and ResNet101) to enhance the performance. Mask R CNNs in our model were first trained on COCO dataset, and then finely tuned using intestinal polyp dataset since a large number of annotated colonoscopy images are not easily accessible. In order to evaluate our proposed model, we used three open intestinal polyp datasets, CVC ClinicDB, ETIS Larib, and CVC ColonDB. Our results show that our transfer learning based ensemble model significantly outperforms state of the art methods.

title: Wireless Capsule Endoscopy: A New Tool for Cancer Screening in the Colon With Deep Learning Based Polyp Recognition
abstract: Accurate recognition of polyps is crucial for early colorectal cancer diagnosis and treatment. Wireless capsule endoscopy (WCE) is a noninvasive, wireless imaging tool that allows direct visualization of the entire colon without discomfort to patients and has the potential to revolutionize the screening workup for colorectal diseases. However, current manual review is laborious and time consuming, requiring the undivided concentration of the gastroenterologist. Computational methods that can assist automated polyp recognition will enhance the outcome both in terms of diagnostic accuracy and efficiency of WCE. This review introduces the computer assisted algorithms as applied to colorectal polyp screening, focusing on the successes of deep learning based strategies in the WCE sequences. We survey key applications of WCE polyp recognition, covering deep learning based image level classification, lesion region detection, and pixel accurate segmentation. We conclude by discussing emerging research challenges, possible trends, and future directions.

title: CAMEL: A Weakly Supervised Learning Framework for Histopathology Image Segmentation
abstract: Histopathology image analysis plays a critical role in cancer diagnosis and treatment. To automatically segment the cancerous regions, fully supervised segmentation algorithms require labor intensive and time consuming labeling at the pixel level. In this research, we propose CAMEL, a weakly supervised learning framework for histopathology image segmentation using only image level labels. Using multiple instance learning (MIL) based label enrichment, CAMEL splits the image into latticed instances and automatically generates instance level labels. After label enrichment, the instance level labels are further assigned to the corresponding pixels, producing the approximate pixel level labels and making fully supervised training of segmentation models possible. CAMEL achieves comparable performance with the fully supervised approaches in both instance level classification and pixel level segmentation on CAMELYON16 and a colorectal adenoma dataset. Moreover, the generality of the automatic labeling methodology may benefit future weakly supervised learning studies for histopathology image analysis.

title: A Convolutional Neural Network based system for Colorectal cancer segmentation on MRI images
abstract: The aim of the study is to present a new Convolutional Neural Network (CNN) based system for the automatic segmentation of the colorectal cancer. The algorithm implemented consists of several steps: a pre processing to normalize and highlights the tumoral area, the classification based on CNNs, and a post processing aimed at reducing false positive elements. The classification is performed using three CNNs: each of them classifies the same regions of interest acquired from three different MR sequences. The final segmentation mask is obtained by a majority voting. Performances were evaluated using a semi automatic segmentation revised by an experienced radiologist as reference standard. The system obtained Dice Similarity Coefficient (DSC) of 0.60, Precision (Pr) of 0.76 and Recall (Re) of 0.55 on the testing set. After applying the leave one out validation, we obtained a median DSC=0.58, Pr=0.74, Re=0.54. The promising results obtained by this system, if validated on a larger dataset, could strongly improve personalized medicine.

title: Colorectal cancer segmentation on MRI images using Convolutional Neural Networks

title: HL FCN: Hybrid loss guided FCN for colorectal cancer segmentation
abstract: Colorectal cancer is among the leading cause of cancer related mortalities. The cancerous regions are conventionally delineated from 3D magnetic resonance images in a voxel wise way for radiotherapy. To ease the manual labeling procedure, which is laborious and time consuming, automatic segmentation methods are highly demanded in clinical practice. However, it is a challenging task due to class imbalance and low contrast appearance of cancerous regions, as well as the hard mimics from complex peritumoral areas. In this paper, we propose a volume to volume fully convolutional network architecture effectively trained with hybrid loss, referred as HL FCN, to automatically segment colorectal cancer regions. Specifically, a novel Dice based hybrid loss is designed under a multi task learning framework to tackle the class imbalance issue and hence improve the discrimination capability. Furthermore, a multi resolution model ensemble strategy is developed to suppress false positives while preserving boundary details. Our method has been extensively validated on 64 cancerous cases using four fold cross validation, outperforming state of the art methods by a significant margin.

title: DoubleU Net: Colorectal Cancer Diagnosis and Gland Instance Segmentation with Text Guided Feature Control
abstract: With the rapid therapeutic advancement in personalized medicine, the role of pathologists for colorectal cancer has greatly expanded from morphologists to clinical consultants. In addition to cancer diagnosis, pathologists are responsible for multiple assessments based on glandular morphology statistics, like selecting appropriate tissue sections for mutation analysis  Therefore, we propose DoubleU Net that determines the initial gland segmentation and diagnoses the histologic grades simultaneously, and then incorporates the diagnosis text data to produce more accurate final segmentation. Our DoubleU Net shows three advantages: (1) Besides the initial segmentation, it offers histologic grade diagnosis and enhanced segmentation for full scale assistance. (2) The textual features extracted from diagnosis data provide high level guidance related to gland morphology, and boost the performance of challenging cases with seriously deformed glands. (3) It can be extended to segmentation tasks with text data like key clinical phrases or pathology descriptions. The model is evaluated on two public colon gland datasets and achieves state of the art performance.

title: Colorectal Cancer Image Segmentation and Classification with Deep Neural Network Based on Information Theory
abstract: Colorectal cancer (CRC) is the development of cancer from the colon or rectum. Microsatellite instability (MSI) status can be considered as an indicator to predict the prognosis of CRC. We employ MSI prediction of CRC image by designing a neural network model of which base network is DeepLabv3+ with OctaveResNet. Additionally, we add a channel sort module to divide a feature map along with channel intensity. Then each feature map goes through distinct convolution paths. Each convolution path is designed based on information theory: the most important feature goes through the lightest convolution path, vice versa. By dividing feature map and applying different amount of convolutional operation, the model can extract features efficiently. In the experiment, total model weight is reduced but accuracy increases.