Selective Scanning for faster Prostate Pathology

Pathology slide scanning microscopes are now commercially available. These microscopes combine a digital microscope with a scanning mechanism to produce high magnification and high resolution images of complete specimens. These digital images have the potential of revolutionizing histopathology. For example, instead of sending glass slides through the mail, pathologists at different locations can consult with each other by sharing image files through the Internet. One of the main obstacles on the way to realizing this possibility is the very large size of the image files that are generated. A single image scanned at 400× resolution requires around 20GB before compression and around 1GB after compression using jpeg2000. As a result, a terabyte hard disk can store only a thousand or so images and sharing such large files over the Internet can result in large delays that would make collaborative consultation impractical. Pathologists, however, hardly ever examine the whole image at high resolution but concentrate on a few relevant fields. If only relevant fields are stored at higher resolution space can be saved, and if these fields of greatest diagnostic interest are transmitted first over the network delay can be reduced. We use a combination of computer vision and machine learning techniques to analyze low magnification images and select those parts of the specimen that should be scanned at high magnification. This is a similar problem to that of detecting cancer, however, the required accuracy is significantly lower. Instead of using computer vision to produce a diagnosis we use computer vision to eliminate those fields of the slide which are unlikely to yeild useful information for human diagnosticians. Examples of prostate tissue sections are shown in Figure 1. Areas of interest marked by an experienced pathologist at low resolution are used as a training set for computer learning (see Figure 2). To discriminate between cancerous and non cancerous fields we generate a 23 bin color histogram for each 20x20 region of the image. We choose to use histograms as different regions have a specific mix of stroma(pink), nuclei(blue) and gland lumens(white) which are explicit in histograms. We use Adaboost to generate a mapping from histograms to scores where high scores correspond to high confidence that the region is cancerous (see Figure 2 (c)). Adaboost [3] is a general-purpose learning algorithm which is particularly powerful when combining many weakly predictive features. The predictive features in this case are the values of the histogram bins. Specifically, we use the alternating decision tree algorithm [2] as implemented in the Jboost open source software package [1]. The classifier built using the histogram is able to separate connective tissue and non-malignant glands from malignant fields. Using the classifier, around 80-90% of the image can be discarded while still keeping all the parts relevant for diagnosis. This decreases the space required for storage and time for transmission by a factor of ten. Our experiments show that our system can discriminate well between malignant and non-malignant fields. However, it is not good at discriminating between malignant fields and hyperplasia. The reason is that color distribution does not provide sufficient information to make this discrimination. We are currently working on features for characterizing the shape of the lumens. We believe that by adding these features into the input for Adaboost we will be able to improve the ability of the system to discriminate between malignant fields and hyperplasia.