Scientific Understanding of Consciousness
Pathology Perception Software in Medical Imaging
Nature 502, S92S94 (31 October 2013)
Medical Imaging Software
Pathology has remained stubbornly analogue and qualitative.. The experienced pathologist's main tools are glass slides, a compound microscope whose design has hardly changed in more than 200 years, and eyes that have seen thousands of tumours. Most of a pathologist's medical decisions are based on morphology, the structural details of cells and tissues revealed under a microscope,
One hurdle to digitizing clinical microscopy is the size and complexity of the images. First, a biopsy is sliced into sections and placed on multiple slides. A digital image of a single slide, magnified under the microscope, has about 10 billion pixels and requires about 30 gigabytes of memory. A typical prostate biopsy, for example, uses more than 20 slides and needs about 600 gigabytes.
That's a lot of information for pathologists to scan through and a lot of data for software to sift. The number and type of cells found in these images is mind-boggling.
Just as people learn by seeing many examples, so can software. In 2011, Harvard Medical School pathologist Andrew Beck built a tool called C-Path (for Computational Pathologist) by feeding learning software with images of breast-cancer biopsies from 248 patients, along with survival data. The software learned to grade the severity of breast cancer and predict patient survival.
A human pathologist who looks at these biopsies under the microscope relies primarily on three features specific to cancer cells to decide how aggressive the tumour is. Do the cell nuclei have an unusual shape? Are the cells dividing? And are the cells connecting with one another as normal, or are they isolated? Pathologists qualitatively score each of these features to determine the tumour grade, a description of how aggressive the tumour is.
The C-Path system works by segmenting images into small regions called 'superpixels'. It identifies cell nuclei and cytoplasm within each superpixel, and compares the qualities of each superpixel such as colour, texture, size and shape with those of its neighbours. For breast cancer, this comparative analysis generates features related to both a sample's global structure and its fine-scale details, such as the average distance between the nuclei of cancer cells and normal cells.
After crunching the training set of images, C-Path came up with 6,642 features, describing not only the tumour cells themselves, which human pathologists focus on, but also the surrounding connective tissue, called the stroma. Indeed, Beck found that the morphology of the stroma was a better predictor of survival than that of the cancer cells alone: an area of stroma that was uniform was associated with a good prognosis, whereas stroma that was infiltrated by epithelial cells indicated more aggressive cancer. Based on its analysis of thousands of features, C-Path was able to predict patient survival more accurately than standard pathological analysis. Beck is now training the software on a broader range of samples, including images of whole slides, and normal breast tissue samples.
It is possible that highly experienced pathologists also look for some of the thousands of features spotted by C-Path but just can't describe them in words. You can compare the experience of spotting a tumour with recognizing your uncle in a photo. You can't articulate exactly how you know he's your uncle is it his nose, eyes, clothing? You just know it's him. But the computer can quantify features in an image, and the analysis is repeatable.
A pathologist at the University of California, Davis, says that software such as C-Path has the potential to replace pathologists in assigning grades to tumours. Others believe that the right place for software is as an aide to help physicians navigate large digital images in real time.
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