Colorectal cancer is one of the leading causes of cancer-related deaths worldwide. It is estimated to be the third most common cancer and the second leading cause of cancer death in both men and women. A development known as a polyp, which may initially be noncancerous (benign) or later turn cancerous, is how colorectal cancer typically manifests.

The everwidening uses of AI in healthcare industry are astonishing. AI can play a significant role in detecting colorectal cancer by assisting in various aspects of the screening and diagnosis process. A few very important examples are:

  • In medical imaging, AI algorithms are capable of finding polyps, lesions, and cancers.
  • Radiologists and pathologists can assess x-rays with the use of AI-powered CAD systems.
  • Using patient data, AI models can evaluate the risk of colorectal cancer.
  • Natural Language Processing (NLP) can be used by AI systems to extract information from medical records and recognize colorectal cancer.

  • A very effective Ki67-CRC module from HS Analysis GmbH can quickly identify both malignant and tumor cells that are on the verge of developing into cancer. Without appropriate data for analyses, detection alone is not sufficient. The HSA KIT software offers the chance to extract all the data into an excel sheet and present the results as reports, graphs, and figures.

(Preview of Ki67-CRC module in HSA KIT)

The preview of HSA KIT above shows the following features:

  • Module name on the top left and the image information
  • A preview window at bottom left for navigation
  • Tool panel on the right side consisting of annotation tools
  • View, ROIs and AI tab on the right:
    • View tab shows all the images selected for the project
    • ROIs tab shows Base ROI and structures of interest
    • AI tab includes custom model imports and option to train a new model

Ki67 Protein

By interacting with numerous proteins involved in DNA replication and cell cycle development, it plays a critical part in controlling cell division.

The present meta analysis demonstrated that high Ki-67 expression is significantly correlated with poor overall survival and disease free survival, indicating that high Ki-67 expression may serve as a valuable predictive method for poor prognosis of colorectal cancer patients.

Ki67 for detecting Colorectal Cancer

Pathologists can assess the proliferation rate of the cancer cells in the colon tumor through staining the tissue sections with antibodies against Ki-67.

The Ki-67 labeling index, indicating the percentage of Ki-67-positive cells, presents important insight into the tumor’s aggressiveness and propensity for growth. A high Ki-67 labeling index suggests more actively dividing cancer cells since it shows a higher proliferation rate.

Colon cancer’s stage and grade can be accurately determined using this information, which can then advise treatment choices and offer prognostic data.

It’s crucial to keep in mind that Ki-67 staining is frequently carried out in combination with other diagnostic markers and histological analysis to thoroughly analyze colon cancer

Risk Factors for Colorectal Cancer

Several factors have been identified as increasing the risk of CRC, including a family history of CRC or polyps, inflammatory bowel disease, certain genetic syndromes, a diet high in red or processed meat, a sedentary lifestyle, obesity, smoking, and heavy alcohol consumption.

Treatment Options for Colorectal Cancer

The treatment options for CRC depend on the stage of the cancer and may include surgery, chemotherapy, radiation therapy, and targeted therapy. Surgery is the most common treatment and may involve removing part or all of the colon or rectum.

Colorectal Cancer Stages

Stage 0: The cancer has only progressed to the lining of the colon or rectum and has not
spread further into the wall or to adjacent lymph nodes.
Stage I: cancer has migrated to the colon or rectum wall but has not spread to
surrounding lymph nodes or other organs.
Stage II: The cancer has spread through the colon or rectum wall but has not migrated
to surrounding lymph nodes or other organs.
Stage III: Cancer has progressed to surrounding lymph nodes but has not moved to
distant organs at this stage.
Stage IV: Cancer has progressed to distant organs such as the liver or lungs at this stage.

The use of HSA Software

The technician by selecting and marking a few amount of tumor cells. Through the use of a synthetic algorithm included into the program that swiftly locates and classifies tumors using R-CNN and Deep Learning models. Early tumor identification is expedited and improved.

Here is an examples of the cells at the beginning before we select tumor cells:

Here are the healthy and unhealthy cells, we need to annotate the infected cells

Here the infected cells are annotated by the annotator

Here is an example the cells before we annotate them and after

Zoom-out view



Zoom-in view



Deep Learning

Deep learning is a sub-set of machine learning which is a subset of AI. AI can merely be a programmed rule that tells the machine to behave in a specific way in certain situations. In other words, artificial intelligence can be nothing more than several if-else statements. In order to reduce the mistakes of its own predictions of the model vs. the facts, machine learning requires significant user input. An extremely powerful framework for deep learning in the modern era, a DL network can express functions of increasing complexity by adding additional layers and units inside each layer.

Given a suitably big model and dataset of labeled training samples, deep learning can typically complete tasks that involve mapping an input sequence to an output sequence quickly and easily. Deep learning requires stronger and better computer hardware in order to operate successfully.

Cost and Loss Function

The reduction of error between predictions and true values is the aim of DL model development. Using loss functions linked to each training example, this is accomplished. The average of the loss function values over all data samples is the cost function. The cost function is improved to lower the DL error. We can attain the greatest outcomes in DL by improving the cost function.

True Positive

This is the structure of good scanned tissue that is interested

Preview around the cells with (blue and yellow colors without including the white area around the cells) which is interested

Preview around the cells with (brown and light brown colors without including the white area around the cells) which is interested

True Nagative

Preview of some dirt on the slide that is out of interested

Preview of the areas with blank white spaces which is out of interested

Whole Slide Imaging (WSI)

All current WSI systems consist of illumination systems, microscope optical components, and a focusing system that precisely places an image on a camera. The final product, or virtual slide, can be assembled in various ways, depending on the particular scanner being used (tiling, line scanning, dual sensor scanning, dynamic focusing, or array scanning). The result is a comprehensive digital rendering of an entire glass slide, visible at resolutions of less than 0.5 μm, that can be examined with interactive software on a computer screen. These sequential images are then combined together to make up one digital image of the slide that will be analyzed in the future.

The pyramid structure Below shows that the base level is the original slide image which has the highest resolution and the other level have different magnifications along with its image down-sampling. Each magnification level includes different types of information, since slide samples structures appear in different ways according to their magnification level. Therefore, it is essential to detect an abnormality and detect it in a specific range of levels.

Structure of WSI Pyramid

Digital Representation of Images

There are several methods for converting color to numbers. The first digital images were frequently created using indexed color. Indexed color is quite small in file size, however it does not show pictures very well. It was quickly replaced as the conventional way by red, green and blue (RGB) color.

We can describe the color picture mathematically by utilizing three-dimensional matrices, one for each of the red, blue, and green colors. An RGB picture, often known as a „true color“ image, can be stored as an m-by-n-by-3 data array. The intensity values from 0 to 255, these values can be represented by one byte or 8 bits. It defining red, green, and blue color components for each single pixel. The color of each pixel is determined by the mix of red, green, and blue intensities kept in each color plane at the pixel’s location. RGB pictures are stored as 24 bit images in graphics file formats, with the red, green, and blue components each being 8 bits. This results in a possible range of 16 million colors.

(a) Corresponding m-by-n-by-3 matrix specifying TrueColor. (b) Color is represented by using varying of red, green and blue light. These are the primary colors by adding percentages of red, green and blue, any color can be created.

Most digital devices, computer screens, and a wide range of editing software use RGB as their primary underlying color model.

Ki67 staining

Ki-67 staining“ is used to identify and quantify the expression of the Ki-67 protein in cells. During different stages of the cell cycle, actively dividing cells express the nuclear protein ki-67, which is linked to cell proliferation. In many different scientific and clinical settings, Ki-67 staining is frequently employed. This is especially true in cancer research, where it is used to evaluate tumor cell proliferation, ascertain the growth fraction of tumors, and help predict prognosis or treatment response.

Ki-67 antigen, which is expressed during the active phases of the cell cycle (G1, S, G2, and mitosis), is detected and mapped out by staining. A faster proliferation rate and enhanced cell division in the tumor are both indicated by elevated Ki-67 expression

Under a microscope, the Ki-67 staining reveals the presence and localization of Ki-67 protein expression within the tissue sample, allowing for the evaluation of cell proliferation rates and identification of actively dividing cells.

Digitalisation of Slides

Pertinent data from the slides, such as cellular morphology, architectural patterns, and Ki-67 expression patterns is collected, to produce precise predictions or classifications. The detection, segmentation, classification, and grading of colorectal cancer are based on histological characteristics, Ki-67 expression, and proliferation indices. Additionally, they offer prognostic data on patient outcomes and therapy options.

The HS Analysis touch

HS Analysis software can be beneficial for a Colorectal Cancer in several ways:

  • Data Analysis: The software can assist in the analysis of various types of data related to colorectal cancer, such as genomic data, gene expression profiles, or protein interactions. It provides tools for data processing, visualization, and statistical analysis, allowing researchers to identify patterns, correlations, and potential biomarkers associated with colorectal cancer. 
  • Biomarker Discovery: HS Analysis software can help identify potential biomarkers for colorectal cancer by analyzing large datasets. By comparing healthy and cancerous samples, the software can identify genes, proteins, or other molecular features that are differentially expressed or associated with the disease. These biomarkers can then be further investigated for diagnostic or therapeutic purposes. 
  • Pathway Analysis: The software can facilitate the analysis of biological pathways and networks involved in colorectal cancer. It can integrate data from multiple sources and apply pathway enrichment analysis to identify key pathways that are dysregulated in the disease. This information can provide insights into the underlying molecular mechanisms of colorectal cancer and potential targets for intervention. 
  • Predictive Modeling: HS Analysis software can be used to build predictive models for colorectal cancer outcomes or treatment response. By utilizing machine learning algorithms, the software can analyze patient data, clinical parameters, and molecular features to develop models that predict prognosis, treatment response, or recurrence risk. These models can aid in personalized medicine approaches and inform treatment decisions.
  • Data Integration: Colorectal cancer research involves the integration of diverse datasets from various sources, including clinical data, molecular data, and imaging data. HS Analysis software can facilitate the integration and analysis of these multidimensional datasets, enabling a comprehensive understanding of the disease and its complexities. 

HS Analysis software provides researchers with powerful tools for data analysis, biomarker discovery, pathway analysis, predictive modeling, and data integration. By leveraging these capabilities, researchers can gain valuable insights into colorectal cancer, enhance their understanding of the disease, and contribute to improved diagnosis, treatment, and patient outcomes.

Custom Modules in HSA KIT

In a lab setting, the HSA KIT is the ultimate tool for developing and training custom deep learning models. With hundreds of scalable modules available, this kit is designed to meet long-term requirements and build flexible solutions that can adapt to changing needs over time. Whether you’re working on a complex project or a simple one, the HSA KIT has everything you need to get started.

From data preprocessing to model building and evaluation, all the necessary tools for successful deep learning projects are included. With user-friendly interface and comprehensive documentation, even beginners can quickly learn how to use this powerful tool. So if you’re looking for an all-in-one solution for your deep learning needs, look no further than the HSA KIT. 

Out of hundreds of custom modules, only 15 can be seen in the image above

Moreover, through a client-centric approach, we leverage the information from iteraction to design, and deliver modules that perfectly align with clients‘ specific goals and objectives; ensuring maximum value and satisfaction.

Better workflow with HSA KIT

At HS Analysis, we take a comprehensive approach to slide analysis. We don’t just analyze the slides themselves, but we also integrate our solutions within the existing infrastructure. This includes connecting to LIS (Laboratory Information System) and naming the slides in a way that makes them easy to save, search, and use for medical purposes. Our goal is to provide a seamless experience for our clients, allowing them to access and utilize the data they need quickly and efficiently.

Traditional workflow of medical image analysis is rather cumbersome where the medical images obtained from CT Scans, MRIs or X-Rays is pre-processed and analysed by a radiologist who evaluates and interprets the images to prepare a very subjective report based on his own knowledge. This might be followed up with a quality assurance by another radiologist for second opinion.

On the other hand, AI based analyses using HSA KIT would provide:

  • Standardised process with subjective/objective analysis
  • Extraction of relevant features from raw data and create meaningful representations for training AI models
  • Module selection and configuration without excessive coding
  • Easy to learn software: Annotate , Train and Automate
  • Quick and efficient analysis of multiple medical images which cuts down time for diagnosis or treament
  • Automated report generation to boost productivity and help physicians or radiologists in evaluation process

Development of the microscopy infrastructure HSA KIT

The HSA KIT software is designed to seamlessly integrate into existing laboratory information systems (LIS), enabling seamless data exchange and interoperability. This integration streamlines the entire workflow, from sample collection to report generation, minimizing manual data entry and reducing the chances of transcription errors. Real-time data synchronization between the HSA KIT software and the LIS ensures that all relevant information is readily accessible, enabling a smooth and efficient diagnostic process.

Furthermore, with HSA Case Veiwer it becomes incredibly easy to store client files in a much more organized way. We provide a smart data management system with HSA KIT that makes structured data available for case-based workflows. Our system is a useful tool to streamline operations because it enables users to quickly organize and analyze data. Furthermore, the sophisticated features guarantee data security and privacy, providing users with comfort when handling sensitive information.

Data storage with HSA Case Viewer

We understand the importance of having accurate and efficient slide analysis software. That’s why we offer a range of features to help you achieve your goals.

  • Our software is compatible with both Linux and Windows operating systems, and it runs smoothly in Docker. With offline functionality, you can work on your slides without an internet connection, but if you need to extend to online use, that’s possible too.
  • We also offer professional integration into your network infrastructure, so you can seamlessly incorporate our software into your existing systems.
  • Our software is designed to work with other programs as well, ensuring that you have all the tools you need at your disposal.
  • Even on weaker computers, our software delivers full performance, so you can analyze slides quickly and accurately.
  • And with the ability to generate reports in CSV and Excel formats, you’ll have all the data you need at your fingertips.
  • Finally, user profile management ensures that each user has their own history and preferences stored for easy access.

Over a short period of time, HS Analysis GmbH has partnered with numerous companies, hospitals and laboratories as clients which provide microscopic slides or data that needs to be analysed. The physical slides can be digitalised to whole slide images (WSI) with the help of HSA KIT software that can be integrated to commonly used microscopes, and data can be retrieved in the form of reports and atomated graphical representaions. This helps to eliminate the extensive process that is usually practiced by medical professionals.

AI based analyses can be way more efficient, however it can only act as a supportive tool to human expertise and never be a complete alternative. AI algorithms have the tendency to produce false positives and false negatives, and may have trouble adapting to a data that deviates from training dataset.

With our advanced technology and expert team of professionals, we are confident in our ability to deliver top-notch slide analysis services that meet the unique needs of each individual client. Whether you’re looking for a one-time analysis or ongoing support, we have the tools and expertise necessary to help you achieve your goals.