48 research outputs found
Machine learning approach for segmenting glands in colon histology images using local intensity and texture features
Colon Cancer is one of the most common types of cancer. The treatment is
planned to depend on the grade or stage of cancer. One of the preconditions for
grading of colon cancer is to segment the glandular structures of tissues.
Manual segmentation method is very time-consuming, and it leads to life risk
for the patients. The principal objective of this project is to assist the
pathologist to accurate detection of colon cancer. In this paper, the authors
have proposed an algorithm for an automatic segmentation of glands in colon
histology using local intensity and texture features. Here the dataset images
are cropped into patches with different window sizes and taken the intensity of
those patches, and also calculated texture-based features. Random forest
classifier has been used to classify this patch into different labels. A
multilevel random forest technique in a hierarchical way is proposed. This
solution is fast, accurate and it is very much applicable in a clinical setup
Micrometastasis Detection Guidance by Whole-Slide Image Texture Analysis in Colorectal Lymph Nodes
Introduction/ Background
Cancer is a disease that affects millions worldwide and accurate determination of whether lymph nodes (LNs) near the primary tumor contain metastatic foci is of critical importance for proper patient management. Histopathological evaluation is the only accepted method to make that determination. However, the current standard of care only examines a single central histological section per LN and yields an unacceptable false-negative rate.
Aims
To help pathologists in their examination we propose a method that extracts textural features from histopathological LN whole slide images (WSI) and then applies support vector machines (SVMs) to automatically identify regions suspicious for metastatic foci.
Methods
The database consisted of WSI from 44 LNs. Sections were stained with hematoxylin-eosin and examined at 20x (0.45μm resolution). Twenty-eight of the LNs were identified by an expert pathologist as positive for cancer (P), and the remaining sixteen were negative (N). This database was divided into two groups. Group 1 (15P and 5N) was used for training and Group 2 (13P and 11N) was used for testing the classification technique. For all analysis each WSI was divided into non-overlapping 1000 x 1000 pixel sub-images that will be referred to as high-power fields (HPFs). For each LN in Group 1, at least one WSI was annotated by a pathologist to identify rectangular, HPF-scale regions as locally cancerous or locally non-cancerous. From these annotated slides, 924 HPFs (462 P and 462 N) were obtained. For each of these HPFs, statistical features based on gray-level co-occurrence matrices [1] and Law’s texture energy measures [2, 3] were extracted from 9 derived images [4]. The extracted features were submitted to a sequential forward selection (SFS) method [5] to select few non-redundant features providing best class separation (cancerous vs. non-cancerous region). Combinations of the selected features were tested on the 924 HPFs using k-fold cross-validation to find those that produced the best results and consequently to train our SVM-based classifier. In Group 2, WSI were not annotated for cancerous and non-cancerous zones on a HPF scale. Each LN, however, had been labeled by a pathologist as positive or negative for cancer. For each WSI, each section was divided into contiguous HPFs, and those which mainly contain fatty tissue, background, and tears were automatically excluded. Each selected HPFs was classified as cancerous or non-cancerous using the previously trained classifier to obtain the total number of cancer-classified per LN. A receiver operating characteristics (ROC) curve was traced by changing the discriminator threshold (T) used to label the LN as P for cancer as a function of the total number of cancer-classified HPFs.
Results
During training, 5 Laws features were selected by SFS. Highly satisfactory k-fold cross-validation with a F-score of 0.996 ± 0.005 was obtained using only 2 statistical features computed at different scales. The ROC curve obtained by applying the SVM-classifier to the test set is shown in the next figure. Two valuable operating points can be identified which both guaranteed no false-negative. At T=11 we got 2 false-positives and an optimal F-score of 0.917, and with a more conservative approach, T=1, we got 7 false-positives and a F-score of 0.759. The top-left part of the slide displayed in next figure would have been proposed to the pathologist as the most suspicious region of the cancerous LN
Capturing Global Spatial Context for Accurate Cell Classification in Skin Cancer Histology
The spectacular response observed in clinical trials of immunotherapy in
patients with previously uncurable Melanoma, a highly aggressive form of skin
cancer, calls for a better understanding of the cancer-immune interface.
Computational pathology provides a unique opportunity to spatially dissect such
interface on digitised pathological slides. Accurate cellular classification is
a key to ensure meaningful results, but is often challenging even with
state-of-art machine learning and deep learning methods.
We propose a hierarchical framework, which mirrors the way pathologists
perceive tumour architecture and define tumour heterogeneity to improve cell
classification methods that rely solely on cell nuclei morphology. The SLIC
superpixel algorithm was used to segment and classify tumour regions in low
resolution H&E-stained histological images of melanoma skin cancer to provide a
global context. Classification of superpixels into tumour, stroma, epidermis
and lumen/white space, yielded a 97.7% training set accuracy and 95.7% testing
set accuracy in 58 whole-tumour images of the TCGA melanoma dataset. The
superpixel classification was projected down to high resolution images to
enhance the performance of a single cell classifier, based on cell nuclear
morphological features, and resulted in increasing its accuracy from 86.4% to
91.6%. Furthermore, a voting scheme was proposed to use global context as
biological a priori knowledge, pushing the accuracy further to 92.8%.
This study demonstrates how using the global spatial context can accurately
characterise the tumour microenvironment and allow us to extend significantly
beyond single-cell morphological classification.Comment: Accepted by MICCAI COMPAY 2018 worksho
Primer for Image Informatics in Personalized Medicine
AbstractImage informatics encompasses the concept of extracting and quantifying information contained in image data. Scenes, what an image contains, come from many imager devices such as consumer electronics, medical imaging systems, 3D laser scanners, microscopes, or satellites. There is a marked increase in image informatics applications as there have been simultaneous advances in imaging platforms, data availability due to social media, and big data analytics. An area ready to take advantage of these developments is personalized medicine, the concept where the goal is tailor healthcare to the individual. Patient health data is computationally profiled against a large of pool of feature-rich data from other patients to ideally optimize how a physician chooses care. One of the daunting challenges is how to effectively utilize medical image data in personalized medicine. Reliable data analytics products require as much automation as possible, which is a difficulty for data like histopathology and radiology images because we require highly trained expert physicians to interpret the information. This review targets biomedical scientists interested in getting started on tackling image analytics. We present high level discussions of sample preparation and image acquisition; data formats; storage and databases; image processing; computer vision and machine learning; and visualization and interactive programming. Examples will be covered using existing open-source software tools such as ImageJ, CellProfiler, and IPython Notebook. We discuss how difficult real-world challenges faced by image informatics and personalized medicine are being tackled with open-source biomedical data and software