Light microscopy applied to the domain of histopathology has traditionally been a two-dimensional imaging modality. Several authors, including the authors of this work, have extended the use of digital microscopy to three dimensions by stacking digital images of serial sections using image-based registration. In this paper, we give an overview of our approach, and of extensions to the approach to register multi-modal data sets such as sets of interleaved histopathology sections with different stains, and sets of histopathology images to radiology volumes with very different appearance. Our approach involves transforming dissimilar images into a multi-channel representation derived from co-occurrence statistics between roughly aligned images.

Registration of histopathology images of consecutive tissue sections stained with different histochemical or immunohistochemical stains is an important step in a number of application areas, such as the investigation of the pathology of a disease, validation of MRI sequences against tissue images, multiscale physical modeling, etc. In each case, information from each stain needs to be spatially aligned and combined to ascertain physical or functional properties of the tissue. However, in addition to the gigabyte-size images and nonrigid distortions present in the tissue, a major challenge for registering differently stained histology image pairs is the dissimilar structural appearance due to different stains highlighting different substances in tissues. In this paper, we address this challenge by developing an unsupervised content classification method that generates multichannel probability images from a roughly aligned image pair. Each channel corresponds to one automatically identified content class. The probability images enhance the structural similarity between image pairs. By integrating the classification method into a multiresolution-block-matching-based nonrigid registration scheme (N. Roberts, D. Magee, Y. Song, K. Brabazon, M. Shires, D. Crellin, N. Orsi, P. Quirke, and D. Treanor, "Toward routine use of 3D histopathology as a research tool," Amer. J. Pathology, vol. 180, no. 5, 2012.), we improve the performance of registering multistained histology images. Evaluation was conducted on 77 histological image pairs taken from three liver specimens and one intervertebral disc specimen. In total, six types of histochemical stains were tested. We evaluated our method against the same registration method implemented without applying the classification algorithm (intensity-based registration) and the state-of-the-art mutual information based registration. Superior results are obtained with the proposed method.

Rationally designed, pH sensitive self-assembling-peptides (SAPs) which are capable of reversibly switching between fluid and gel phases in response to environmental triggers are potentially useful injectable scaffolds for skeletal tissue engineering applications. SAP P11-4 (CH3COQQRFEWEFEQQNH2) has been shown to nucleate hydroxyapatite mineral de novo and has been used in dental enamel regeneration. We hypothesised that addition of mesenchymal stromal cells (MSCs) would enhance the in vivo effects of P11-4 in promoting skeletal tissue repair. Cranial defects were created in athymic rats and filled with either Bio-Oss ® (anorganic bone chips) or P11-4 ± human dental pulp stromal cells (HDPSCs). Unfilled defects served as controls. After 4 weeks, only those defects filled with P11-4 alone showed significantly increased bone regeneration (almost complete healing), compared to unfilled control defects, as judged using quantitative micro-CT, histology and immunohistochemistry. In silico modelling indicated that fibril formation may be essential for any mineral nucleation activity. Taken together, these data suggest that self-assembling peptides are a suitable scaffold for regeneration of bone tissue in a one step, cell-free therapeutic approach.

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