This paper presents a fully automatic approach to grade intermediate prostate malignancy with hematoxylin and eosin-stained whole slide images. Deep learning architectures such as convolutional neural networks have been utilized in the domain of histopathology for automated carcinoma detection and classification. However, few work show its power in discriminating intermediate Gleason patterns, due to sporadic distribution of prostate glands on stained surgical section samples. We propose optimized hematoxylin decomposition on localized images, followed by convolutional neural network to classify Gleason patterns 3+4 and 4+3 without handcrafted features or gland segmentation. Crucial glands morphology and structural relationship of nuclei are extracted twice in different color space by the multi-scale strategy to mimic pathologists’ visual examination. Our novel classification scheme evaluated on 169 whole slide images yielded a 70.41% accuracy and corresponding area under the receiver operating characteristic curve of 0.7247.
Digital histopathology images with more than 1 Gigapixel are drawing more and more attention in clinical,
biomedical research, and computer vision fields. Among the multiple observable features spanning multiple
scales in the pathology images, the nuclear morphology is one of the central criteria for diagnosis and grading.
As a result it is also the mostly studied target in image computing. Large amount of research papers have
devoted to the problem of extracting nuclei from digital pathology images, which is the foundation of any
further correlation study. However, the validation and evaluation of nucleus extraction have yet been formulated
rigorously and systematically. Some researches report a human verified segmentation with thousands of nuclei,
whereas a single whole slide image may contain up to million. The main obstacle lies in the difficulty of obtaining
such a large number of validated nuclei, which is essentially an impossible task for pathologist. We propose a
systematic validation and evaluation approach based on large scale image synthesis. This could facilitate a more
quantitatively validated study for current and future histopathology image analysis field.