Research advances of tissue-derived extracellular vesicles in cancers.

BACKGROUND: Extracellular vesicles (EVs) can mediate cell-to-cell communication and affect various physiological and pathological processes in both parent and recipient cells. Currently, extensive research has focused on the EVs derived from cell cultures and various body fluids. However, insufficient attention has been paid to the EVs derived from tissues. Tissue EVs can reflect the microenvironment of the specific tissue and the cross-talk of communication among different cells, which can provide more accurate and comprehensive information for understanding the development and progression of diseases. METHODS: We review the state-of-the-art technologies involved in the isolation and purification of tissue EVs. Then, the latest research progress of tissue EVs in the mechanism of tumor occurrence and development is presented. And finally, the application of tissue EVs in the clinical diagnosis and treatment of cancer is anticipated. RESULTS: We evaluate the strengths and weaknesses of various tissue processing and EVs isolation methods, and subsequently analyze the significance of protein characterization in determining the purity of tissue EVs. Furthermore, we focus on outlining the importance of EVs derived from tumor and adipose tissues in tumorigenesis and development, as well as their potential applications in early tumor diagnosis, prognosis, and treatment. CONCLUSION: When isolating and characterizing tissue EVs, the most appropriate protocol needs to be specified based on the characteristics of different tissues. Tissue EVs are valuable in the diagnosis, prognosis, and treatment of tumors, and the potential risks associated with tissue EVs need to be considered as therapeutic agents.

Adaptive denoising hyperspectral data for visualization enhancement of intraoperative tissue.

The vast amount of reflectance information obtained from the hyperspectral imaging devices offers great opportunities for investigating the function and structure of human tissue. However, the captured hyperspectral data often contain various noises due to the intrinsic imperfection of associated electrical and optical imaging components. This work proposed an automatic total variation algorithm to suppress the noises while preserving the details of the spectral and spatial information. The variation of spectral images at neighboring bands was calculated for regulating the total variation of hyperspectral data so that the spectral-dependent noises can be treated differentially across all bands. Experimental results demonstrated that the proposed method could effectively remove the spectral noises, especially near the ends of those extreme bands. The noise suppressed hyperspectral data could then be used for the visualization enhancement on pathophysiological conditions of intraoperative observed anatomies such as the vessels of brain tissues.

Band selection for mapping chromophores of skin tissue.

A numerical approach has been proposed to identify bands for optimally estimating the concentration of three types of viable chromophores within biological tissue. The bands are determined according to the condition number of absorption matrix associated with the attenuation coefficients of chromophores. The effectiveness of different sets of selected band combination was verified by using the spectral reflectance images of skin tissue acquired from standard forearm vascular occlusion tests via a spectroradiometer. Experimental results demonstrated that the concentration of chromophores within skin tissue could be estimated correctly and robustly only when the bands were deliberately selected.

Estimating rotation angle from asymmetric projection of chest.

BACKGROUND: Manual or machine-based analysis of chest radiographs needs the images acquired with technical adequacy. Currently, the equidistance between the medial end of clavicles and the center of spinous processes serves as the only criterion to assess whether a frontal PA chest radiograph is taken with any rotation. However, this measurement is normally difficult to implement because there exists overlapping of anatomies within the region. Moreover, there is no way available to predict exact rotating angles even the distances were correctly measured from PA chest radiographs. OBJECTIVE: To quantitatively assess positioning adequacy of PA chest examination, this study proposes and investigates a new method to estimate rotation angles from asymmetric projection of thoracic cage on radiographs. METHOD: By looking into the process of radiographic projection, generalized expressions have been established to correlate rotating angles of thorax with projection difference of left and right sides of thoracic cage. A trunk phantom with different positioning angles is employed to acquire radiographs as standard reference to verify the theoretical expressions. RESULTS: The angles estimated from asymmetric projections of thoracic cage yield good agreement with those actual rotated angles, and an approximate linear relationship exists between rotation angle and asymmetric projection of thoracic cage. Under the experimental projection settings, every degree of rotation corresponds to the width difference of two sides of thoracic cage around 13-14 pixels. CONCLUSION: The proposed new method may be used to quantify rotating angles of chest and assess image quality for thoracic radiographic examination.

Validation of radiographic quality of simulation software - ImaSim.

BACKGROUND: Virtual radiographic simulation has been found educationally effective for students to practice their clinical examinations remotely or online. A free available virtual simulator-ImaSim has received particular attention for radiographic science education because of its portability, free of charge and no constrain of location and physical facility. However, it lacks evidence to validate this virtual simulation software to faithfully reproduce radiographs comparable to that taken from a real X-ray machine to date. OBJECTIVE: To evaluate image quality of the virtual radiographs produced by the ImaSim. Thus, the deployment of this radiographic simulation software for teaching and experimental studying of radiography can be justified. METHODS: A real medical X-ray examination machine is employed to scan three standard QC phantoms to produce radiographs for comparing to the corresponding virtual radiographs generated by ImaSim software. The high and low range of radiographic contrast and comprehensive contrast-detail performance are considered to characterize the radiographic quality of the virtual simulation software. RESULTS: ImaSim software can generate radiographs with a contrast ranging from 30% to 0.8% and a spatial resolution as low as 0.6mm under the selected exposure setting condition. The characteristics of contrast and spatial resolution of virtual simulation generally agree with that of real medical X-ray examination machine. CONCLUSION: ImaSim software can be used to simulate a radiographic imaging process to generate radiographs with contrast and detail detectability comparable to those produced by a real X-ray imaging machine. Therefore, it can be adopted as a flexible educational tool for proof of concept and experimental design in radiography.

3D augmented fundus images for identifying glaucoma via transferred convolutional neural networks.

PURPOSE: Glaucoma is a chronic and irreversible retinopathy threatening the vision of millions of patients around the world. Its early diagnosis and treatment can help to prolong the period of sight deterioration from no visual impairment to blindness, whereas the screening and diagnosis of glaucoma in clinical remains challenging because some key assessment criteria like cup-to-disc ratio is limited by subjective analysis and intra- and inter-observer variability. This paper exploits the potential of new augmented image data of the optic nerve head (ONH) combining with the latest deep learning networks to achieve better diagnosis of glaucoma. METHODS: This paper explores the potential value of additional three-dimensional topographic map of the optic nerve head proceeded by the latest deep learning approaches, i.e. convolutional neural networks to improve the diagnosis efficiency. Specifically, 3D topography map of the ONH and RGB fundus image has been used to train the transferred AlexNet and VGG-16 networks. The diagnostic performance is compared to those achieved by using the 2D fundus images only. RESULTS: The 3D topographic map of ONH reconstructed from the shape from shading method provides better visualization of the structure of optic cup and disc. These new enhanced dataset was employed to train the proposed deep learning networks and finally achieve diagnostic accuracy of 94.3% which is superior to the networks trained via 2D conventional images. CONCLUSION: Employing the deep learning neural networks with augmented 3D images can increase the accuracy of automatic separating glaucoma and non-glaucoma fundus images. It may be used as an objective tool in developing computer assisted diagnosis systems for assessment of glaucoma.

Evaluation on an Optical Scanning Device for Skin Profile Measurement.

This paper describes experimental evaluations of an optical scanning device for skin surface recovery using multiple light source photometric stereo method. The portable optical device based on the principle of six-light photometric stereo was developed and subjected to evaluation and advancement through clinical trials for the purpose of monitoring skin conditions. As the device can provide objective topographic data for the description of the skin surface condition, the evaluation processes are mainly applied on skin in vitro and in vivo and compared with a commercial product, PRIMOS, which has been so far considered as a standard device used for skin surface measurement. The results of the experiment show that the topography measured by the device is significantly closer to that of the ground truth. Meanwhile, the new optical scanning device demonstrates better performance in measuring skin surface in vivo, superior to that of the PRIMOS.

Phenotyping for the dynamics of field wheat root system architecture.

We investigated a method to quantify field-state wheat RSA in a phenotyping way, depicting the 3D topology of wheat RSA in 14d periods. The phenotyping procedure, proposed for understanding the spatio-temporal variations of root-soil interaction and the RSA dynamics in the field, is realized with a set of indices of mm scale precision, illustrating the gradients of both wheat root angle and elongation rate along soil depth, as well as the foraging potential along the side directions. The 70d was identified as the shifting point distinguishing the linear root length elongation from power-law development. Root vertical angle in the 40 mm surface soil layer was the largest, but steadily decreased along the soil depth. After 98d, larger root vertical angle appeared in the deep soil layers. PAC revealed a stable root foraging potential in the 0-70d period, which increased rapidly afterwards (70-112d). Root foraging potential, explained by MaxW/MaxD ratio, revealed an enhanced gravitropism in 14d period. No-till post-paddy wheat RLD decreased exponentially in both depth and circular directions, with 90% roots concentrated within the top 20 cm soil layer. RER along soil depth was either positive or negative, depending on specific soil layers and the sampling time.

Combination of 3D skin surface texture features and 2D ABCD features for improved melanoma diagnosis.

Two-dimensional asymmetry, border irregularity, colour variegation and diameter (ABCD) features are important indicators currently used for computer-assisted diagnosis of malignant melanoma (MM); however, they often prove to be insufficient to make a convincing diagnosis. Previous work has demonstrated that 3D skin surface normal features in the form of tilt and slant pattern disruptions are promising new features independent from the existing 2D ABCD features. This work investigates that whether improved lesion classification can be achieved by combining the 3D features with the 2D ABCD features. Experiments using a nonlinear support vector machine classifier show that many combinations of the 2D ABCD features and the 3D features can give substantially better classification accuracy than using (1) single features and (2) many combinations of the 2D ABCD features. The best 2D and 3D feature combination includes the overall 3D skin surface disruption, the asymmetry and all the three colour channel features. It gives an overall 87.8 % successful classification, which is better than the best single feature with 78.0 % and the best 2D feature combination with 83.1 %. These demonstrate that (1) the 3D features have additive values to improve the existing lesion classification and (2) combining the 3D feature with all the 2D features does not lead to the best lesion classification. The two ABCD features not selected by the best 2D and 3D combination, namely (1) the border feature and (2) the diameter feature, were also studied in separate experiments. It found that inclusion of either feature in the 2D and 3D combination can successfully classify 3 out of 4 lesion groups. The only one group not accurately classified by either feature can be classified satisfactorily by the other. In both cases, they have shown better classification performances than those without the 3D feature in the combinations. This further demonstrates that (1) the 3D feature can be used to improve the existing 2D-based diagnosis and (2) including the 3D feature with subsets of the 2D features can be used in distinguishing different benign lesion classes from MM. It is envisaged that classification performance may be further improved if different 2D and 3D feature subsets demonstrated in this study are used in different stages to target different benign lesion classes in future studies.

Distribution quantification on dermoscopy images for computer-assisted diagnosis of cutaneous melanomas.

Computerised analysis on skin lesion images has been reported to be helpful in achieving objective and reproducible diagnosis of melanoma. In particular, asymmetry in shape, colour and structure reflects the irregular growth of melanin under the skin and is of great importance for diagnosing the malignancy of skin lesions. This paper proposes a novel asymmetry analysis based on a newly developed pigmentation elevation model and the global point signatures (GPSs). Specifically, the pigmentation elevation model was first constructed by computer-based analysis of dermoscopy images, for the identification of melanin and haemoglobin. Asymmetry of skin lesions was then assessed through quantifying distributions of the pigmentation elevation model using the GPSs, derived from a Laplace-Beltrami operator. This new approach allows quantifying the shape and pigmentation distributions of cutaneous lesions simultaneously. Algorithm performance was tested on 351 dermoscopy images, including 88 malignant melanomas and 263 benign naevi, employing a support vector machine (SVM) with tenfold cross-validation strategy. Competitive diagnostic results were achieved using the proposed asymmetry descriptor only, presenting 86.36 % sensitivity, 82.13 % specificity and overall 83.43 % accuracy, respectively. In addition, the proposed GPS-based asymmetry analysis enables working on dermoscopy images from different databases and is approved to be inherently robust to the external imaging variations. These advantages suggested that the proposed method has good potential for follow-up treatment.

Unsupervised sub-segmentation for pigmented skin lesions.

BACKGROUND: Early identification of malignant melanoma with the surgical removal of thin lesions is the most effective treatment for skin cancers. A computer-aided diagnostic system assists to improve the diagnostic accuracy, where segmenting lesion from normal skin is usually considered as the first step. One of the challenges in the automated segmentation of skin lesions arises from the fact that darker areas within the lesion should be considered separate from the more general suspicious lesion as a whole, because these pigmented areas can provide significant additional diagnostic information. METHODS: This paper presents, for the first time, an unsupervised segmentation scheme to allow the isolation of normal skin, pigmented skin lesions, and interesting darker areas inside the lesion simultaneously. An adaptive mean-shift is first applied with a 5D spatial colour-texture feature space to generate a group of homogenous regions. Then the sub-segmentation maps are calculated by integrating maximal similarity-based region merging and the kernel k-means algorithm, where the number of segments is defined by a cluster validity measurement. RESULTS: The proposed method has been validated extensively on both normal digital photographs and dermoscopy images, which demonstrates competitive performance in achieving automatic segmentation. The isolated dark areas have proved helpful in the discrimination of malignant melanomas from atypical benign nevi. Compared with the results obtained from the asymmetry measure of the entire lesion, the asymmetry distribution of the isolated dark areas helped increase the accuracy of the identification of malignant melanoma from 65.38% to 73.07%, and this classification accuracy reached 80.77% on integrating both asymmetry descriptors. CONCLUSION: The proposed segmentation scheme gives the lesion boundary closed to the manual segmentation obtained by experienced dermatologists. The initial classification results indicate that the study of the distributions of darker areas inside the lesions is very promising in characterizing melanomas.

Biological indexes based reflectional asymmetry for classifying cutaneous lesions.

This paper proposes a novel reflectional asymmetry descriptor to quantize the asymmetry of the cutaneous lesions for the discrimination of malignant melanoma from benign nevi. A pigmentation elevation model of the biological indexes is first constructed, and then the asymmetry descriptor is computed by minimizing the histogram difference of the global point signatures of the pigmentation model. Melanin and Erythema Indexes are used instead of the original intensities in colour space to characterize the pigmentation distribution of the cutaneous lesions. 311 dermoscopy images are used to validate the algorithm performance, where 88.50% sensitivity and 81.92% specificity have been achieved when employing an SVM classifier.

Obtaining malignant melanoma indicators through statistical analysis of 3D skin surface disruptions.

BACKGROUND/PURPOSE: It has been observed that disruptions in skin patterns are larger for malignant melanoma (MM) than benign lesions. In order to extend the classification results achieved for 2D skin patterns, this work intends to investigate the feasibility of lesion classification using 3D skin surface texture, in the form of surface normals acquired from a previously built six-light photometric stereo device. MATERIAL AND METHODS: The proposed approach seeks to separate MM from benign lesions through analysis of the degree of surface disruptions in the tilt and slant direction of surface normals, so called skin tilt pattern and skin slant pattern. A 2D Gaussian function is used to simulate a normal region of skin for comparison with a lesion's observed tilt and slant patterns. The differences associated with the two patterns are estimated as the disruptions in the tilt and slant pattern respectively for lesion classification. RESULTS: Preliminary studies on 11 MMs and 28 benign lesions have given Receiver operating characteristic areas of 0.73 and 0.85 for tilt and slant pattern, respectively, which are better than 0.65 previously obtained for the skin line direction using the same samples. CONCLUSIONS: This paper has demonstrated an important application of 3D skin texture for computer-assisted diagnosis of MM in vivo. By taking advantage of the extra dimensional information, preliminary studies suggest that some improvements over the existing 2D skin line pattern approach for the differentiation between MM and benign lesions.

Reflectance of human skin using colour photometric stereo: with particular application to pigmented lesion analysis.

BACKGROUND/PURPOSE: The optical appearance of human skin is highly dependent on the interaction between the illumination (type and position), observer position and the skin surface structure. Different currently available photographic techniques record different aspects of this appearance, each providing its own incomplete description. This limits their usefulness, especially for pigmented skin lesion diagnosis. In this paper a new, easy to use, low-cost photographic method is described,which aims to generate an efficiently encoded yet reasonably complete representation of skin appearance. MATERIAL AND METHODS: A prototype hand-held camera was developed that rapidly acquires six colour images, each with the skin illuminated from a different direction. A novel photometric stereo processing was used to combine these into a colour image of the skin's diffuse reflectance, independent of the skin surface topography, as well as a separate representation of that topography in the form of a surface gradient image. Images of four clinical pigmented skin lesions were evaluated in comparison with conventional digital photographs by both visual judgement and automated lesion boundary detection. RESULTS: The new colour reflectance images were free from the effects of topographical shading, shadowing and specular reflections. Lesion boundaries obtained automatically from the reflectance images were always closer to the outline drawn by a dermatologist than those obtained from conventional photographs. Finally, recombining the colour reflectance and surface gradient data to form a virtual image of the skin surface that is highly realistic in appearance. CONCLUSIONS: The new colour photometric stereo camera produces images of skin and skin tumours in which the reflectance information that is related to subsurface pigment distribution is separated from the surface topographic information. The total information generated by the system, for use in visual or automated analysis, is potentially greater than that for either conventional photography or dermatoscopy alone. Its further development and broader clinical evaluation are warranted to determine its usefulness and role in a wide range of dermatological tasks, including tele-dermatology applications.

[The research on accuracy of computer-assisted surgery].

The computer-assisted surgery system is a complex system. All of the errors can be attributed to the loss of correspondence between the world coordinate system in the operation room and the virtual world coordinate system obtained from the multi-model medical images. The system's accuracy is composed of the accuracy of the localizer and that of registration. In order to improve the system accuracy, we analyse most of the possible error sources. The accuracy of the localizer affects deeply the registration between the intra-operation and pre-operation data. The localizer is the most basic and important part for a computer-assisted surgery system. We give a comprehensive possible error source at the end of the paper.