Integration of magnetic resonance imaging (MRI) and other imaging modalities is promising to furnish complementary information for accurate cancer diagnosis and imaging-guided therapy. However, most gadolinium (Gd)-chelator MR contrast agents are limited by their relatively low relaxivity and high risk of released-Gd-ions-associated toxicity. Herein, a radionuclide- Cu-labeled doxorubicin-loaded polydopamine (PDA)-gadolinium-metallofullerene core-satellite nanotheranostic agent (denoted as CDPGM) is developed for MR/photoacoustic (PA)/positron emission tomography (PET) multimodal imaging-guided combination cancer therapy. In this system, the near-infrared (NIR)-absorbing PDA acts as a platform for the assembly of different moieties; Gd N@C , a kind of gadolinium metallofullerene with three Gd ions in one carbon cage, acts as a satellite anchoring on the surface of PDA. The as-prepared CDPGM NPs show good biocompatibility, strong NIR absorption, high relaxivity (r = 14.06 mM s ), low risk of release of Gd ions, and NIR-triggered drug release. In vivo MR/PA/PET multimodal imaging confirms effective tumor accumulation of the CDPGM NPs. Moreover, upon NIR laser irradiation, the tumor is completely eliminated with combined chemo-photothermal therapy. These results suggest that the CDPGM NPs hold great promise for cancer theranostics.

In this paper, a multiscale convolutional network (MSCN) and graph-partitioning-based method is proposed for accurate segmentation of cervical cytoplasm and nuclei. Specifically, deep learning via the MSCN is explored to extract scale invariant features, and then, segment regions centered at each pixel. The coarse segmentation is refined by an automated graph partitioning method based on the pretrained feature. The texture, shape, and contextual information of the target objects are learned to localize the appearance of distinctive boundary, which is also explored to generate markers to split the touching nuclei. For further refinement of the segmentation, a coarse-to-fine nucleus segmentation framework is developed. The computational complexity of the segmentation is reduced by using superpixel instead of raw pixels. Extensive experimental results demonstrate that the proposed cervical nucleus cell segmentation delivers promising results and outperforms existing methods.

Accurate segmentation of cervical cells in Pap smear images is an important step in automatic pre-cancer identification in the uterine cervix. One of the major segmentation challenges is overlapping of cytoplasm, which has not been well-addressed in previous studies. To tackle the overlapping issue, this paper proposes a learning-based method with robust shape priors to segment individual cell in Pap smear images to support automatic monitoring of changes in cells, which is a vital prerequisite of early detection of cervical cancer. We define this splitting problem as a discrete labeling task for multiple cells with a suitable cost function. The labeling results are then fed into our dynamic multi-template deformation model for further boundary refinement. Multi-scale deep convolutional networks are adopted to learn the diverse cell appearance features. We also incorporated high-level shape information to guide segmentation where cell boundary might be weak or lost due to cell overlapping. An evaluation carried out using two different datasets demonstrates the superiority of our proposed method over the state-of-the-art methods in terms of segmentation accuracy.

In this paper, we present a novel framework for dermoscopy image recognition via both a deep learning method and a local descriptor encoding strategy. Specifically, the deep representations of a rescaled dermoscopy image are first extracted via a very deep residual neural network (ResNet) pre-trained on a large natural image dataset. Then these local deep descriptors are aggregated by orderless visual statistic features based on fisher vector (FV) encoding to build a global image representation. Finally, the FV encoded representations are used to classify melanoma images using a support vector machine (SVM) with a Chi-squared kernel. Our proposed method is capable of generating more discriminative features to deal with large variations within melanoma classes as well as small variations between melanoma and non-melanoma classes with limited training data. Extensive experiments are performed to demonstrate the effectiveness of our proposed method. Comparisons with state-of-the-art methods show the superiority of our method using the publicly available ISBI 2016 Skin lesion challenge dataset.

Persistent luminescence nanoparticles (PLNPs) have been used for bioimaging without autofluorescence background interference, but the poor afterglow performance impedes their further applications in cancer therapy. To overcome the Achilles' heel of PLNPs, herein we report the construction of injectable persistent luminescence implants (denoted as PL implants) as a built-in excitation source for efficient repeatable photodynamic therapy (PDT). The injectable ZGC (ZnGaO:Cr) PL implants were prepared by dissolving ZGC PLNPs in poly(lactic-co-glycolic acid)/N-methylpyrrolidone oleosol, which demonstrated much stronger persistent luminescence (PersL) intensity and longer PersL lifetime than that of ZGC PLNPs both in vitro and in vivo. More importantly, the intratumorally fixed ZGC PL implants can serve as a built-in excitation source for repeatable light emitting diode (LED) and PersL-excited PDT upon and after periodic LED irradiation, which leads to the overall improvement of therapeutic effectiveness for efficient tumor growth suppression. This work represents efficient repeatable PDT based on the injectable yet periodically rechargeable ZGC PL implants.

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Current automation-assisted technologies for screening cervical cancer mainly rely on automated liquid-based cytology slides with proprietary stain. This is not a costefficient approach to be utilized in developing countries. In this article, we propose the first automation-assisted system to screen cervical cancer in manual liquid-based cytology (MLBC) slides with hematoxylin and eosin (H&E) stain, which is inexpensive and more applicable in developing countries. This system consists of three main modules: image acquisition, cell segmentation, and cell classification. First, an autofocusing scheme is proposed to find the global maximum of the focus curve by iteratively comparing image qualities of specific locations. On the autofocused images, the multiway graph cut (GC) is performed globally on the a* channel enhanced image to obtain cytoplasm segmentation. The nuclei, especially abnormal nuclei, are robustly segmented by using GC adaptively and locally. Two concave-based approaches are integrated to split the touching nuclei. To classify the segmented cells, features are selected and preprocessed to improve the sensitivity, and contextual and cytoplasm information are introduced to improve the specificity. Experiments on 26 consecutive image stacks demonstrated that the dynamic autofocusing accuracy was 2.06 lm. On 21 cervical cell images with nonideal imaging condition and pathology, our segmentation method achieved a 93% accuracy for cytoplasm, and a 87.3% F-measure for nuclei, both outperformed state of the art works in terms of accuracy. Additional clinical trials showed that both the sensitivity (88.1%) and the specificity (100%) of our system are satisfyingly high. These results proved the feasibility of automation-assisted cervical cancer screening in MLBC slides with H&E stain, which is highly desirable in community health centers and small hospitals. V C 2013 International Society for Advancement of Cytometry

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