A resampling-based Markovian model for automated colon cancer diagnosis

A comprehensive survey of intestine histopathological image analysis using machine vision approaches

Colorectal Cancer (CRC) is currently one of the most common and deadly cancers. CRC is the third most common malignancy and the fourth leading cause of cancer death worldwide. It ranks as the second most frequent cause of cancer-related deaths in the United States and other developed countries. Histopathological images contain sufficient phenotypic information, they play an indispensable role in the diagnosis and treatment of CRC. In order to improve the objectivity and diagnostic efficiency for image analysis of intestinal histopathology, Computer-aided Diagnosis (CAD) methods based on machine learning (ML) are widely applied in image analysis of intestinal histopathology. In this investigation, we conduct a comprehensive study on recent ML-based methods for image analysis of intestinal histopathology. First, we discuss commonly used datasets from basic research studies with knowledge of intestinal histopathology relevant to medicine. Second, we introduce traditional ML methods commonly used in intestinal histopathology, as well as deep learning (DL) methods. Then, we provide a comprehensive review of the recent developments in ML methods for segmentation, classification, detection, and recognition, among others, for histopathological images of the intestine. Finally, the existing methods have been studied, and the application prospects of these methods in this field are given.

FEEDNet: a feature enhanced encoder-decoder LSTM network for nuclei instance segmentation for histopathological diagnosis

Objective. Automated cell nuclei segmentation is vital for the histopathological diagnosis of cancer. However, nuclei segmentation from ‘hematoxylin and eosin’ (HE) stained ‘whole slide images’ (WSIs) remains a challenge due to noise-induced intensity variations and uneven staining. The goal of this paper is to propose a novel deep learning model for accurately segmenting the nuclei in HE-stained WSIs. Approach. We introduce FEEDNet, a novel encoder-decoder network that uses LSTM units and ‘feature enhancement blocks’ (FE-blocks). Our proposed FE-block avoids the loss of location information incurred by pooling layers by concatenating the downsampled version of the original image to preserve pixel intensities. FEEDNet uses an LSTM unit to capture multi-channel representations compactly. Secondly, for datasets that provide class information, we train a multiclass segmentation model, which generates masks corresponding to each class at the output. Using this information, we generate more accurate binary masks than that generated by conventional binary segmentation models. Main results. We have thoroughly evaluated FEEDNet on CoNSeP, Kumar, and CPM-17 datasets. FEEDNet achieves the best value of PQ (panoptic quality) on CoNSeP and CPM-17 datasets and the second best value of PQ on the Kumar dataset. The 32-bit floating-point version of FEEDNet has a model size of 64.90 MB. With INT8 quantization, the model size reduces to only 16.51 MB, with a negligible loss in predictive performance on Kumar and CPM-17 datasets and a minor loss on the CoNSeP dataset. Significance. Our proposed idea of generalized class-aware binary segmentation is shown to be accurate on a variety of datasets. FEEDNet has a smaller model size than the previous nuclei segmentation networks, which makes it suitable for execution on memory-constrained edge devices. The state-of-the-art predictive performance of FEEDNet makes it the most preferred network. The source code can be obtained from https://github.com/CandleLabAI/FEEDNet.

Computer-based image studies on tumor nests mathematical features of breast cancer and their clinical prognostic value

Background

The expending and invasive features of tumor nests could reflect the malignant biological behaviors of breast invasive ductal carcinoma. Useful information on cancer invasiveness hidden within tumor nests could be extracted and analyzed by computer image processing and big data analysis.

Methods

Tissue microarrays from invasive ductal carcinoma (n = 202) were first stained with cytokeratin by immunohistochemical method to clearly demarcate the tumor nests. Then an expert-aided computer analysis system was developed to study the mathematical and geometrical features of the tumor nests. Computer recognition system and imaging analysis software extracted tumor nests information, and mathematical features of tumor nests were calculated. The relationship between tumor nests mathematical parameters and patients' 5-year disease free survival was studied.

Results

There were 8 mathematical parameters extracted by expert-aided computer analysis system. Three mathematical parameters (number, circularity and total perimeter) with area under curve >0.5 and 4 mathematical parameters (average area, average perimeter, total area/total perimeter, average (area/perimeter)) with area under curve <0.5 in ROC analysis were combined into integrated parameter 1 and integrated parameter 2, respectively. Multivariate analysis showed that integrated parameter 1 (P = 0.040) was independent prognostic factor of patients' 5-year disease free survival. The hazard risk ratio of integrated parameter 1 was 1.454 (HR 95% CI [1.017–2.078]), higher than that of N stage (HR 1.396, 95% CI [1.125–1.733]) and hormone receptor status (HR 0.575, 95% CI [0.353–0.936]), but lower than that of histological grading (HR 3.370, 95% CI [1.125–5.364]) and T stage (HR 1.610, 95% CI [1.026 –2.527]).

Conclusions

This study indicated integrated parameter 1 of mathematical features (number, circularity and total perimeter) of tumor nests could be a useful parameter to predict the prognosis of early stage breast invasive ductal carcinoma.

A recent survey on colon cancer detection techniques

Colon cancer causes deaths of about half a million people every year. Common method of its detection is histopathological tissue analysis, which, though leads to vital diagnosis, is significantly correlated to the tiredness, experience, and workload of the pathologist. Researchers have been working since decades to get rid of manual inspection, and to develop trustworthy systems for detecting colon cancer. Several techniques, based on spectral/spatial analysis of colon biopsy images, and serum and gene analysis of colon samples, have been proposed in this regard. Due to rapid evolution of colon cancer detection techniques, a latest review of recent research in this field is highly desirable. The aim of this paper is to discuss various colon cancer detection techniques. In this survey, we categorize the techniques on the basis of the adopted methodology and underlying data set, and provide detailed description of techniques in each category. Additionally, this study provides an extensive comparison of various colon cancer detection categories, and of multiple techniques within each category. Further, most of the techniques have been evaluated on similar data set to provide a fair performance comparison. Analysis reveals that neither of the techniques is perfect; however, research community is progressively inching toward the finest possible solution.