Skin Lesion Correspondence Localization in Total Body Photography

Longitudinal tracking of skin lesions - finding correspondence, changes in morphology, and texture - is beneficial to the early detection of melanoma. However, it has not been well investigated in the context of full-body imaging. We propose a novel framework combining geometric and texture information to localize skin lesion correspondence from a source scan to a target scan in total body photography (TBP). Body landmarks or sparse correspondence are first created on the source and target 3D textured meshes. Every vertex on each of the meshes is then mapped to a feature vector characterizing the geodesic distances to the landmarks on that mesh. Then, for each lesion of interest (LOI) on the source, its corresponding location on the target is first coarsely estimated using the geometric information encoded in the feature vectors and then refined using the texture information. We evaluated the framework quantitatively on both a public and a private dataset, for which our success rates (at 10 mm criterion) are comparable to the only reported longitudinal study. As full-body 3D capture becomes more prevalent and has higher quality, we expect the proposed method to constitute a valuable step in the longitudinal tracking of skin lesions.Comment: MICCAI-202

Objective Assessment of Area and Erythema of Psoriasis Lesion Using Digital Imaging and Colourimetry

Psoriasis is a non-contagious skin disease which typically consists of red plaques covered by silvery-white scales. It affects about 3% of world population. During treatment, dermatologists monitor the extent of psoriasis continuously to ascertain treatment efficacy. Psoriasis Area and Severity Index (PAS!) is the current gold standard method used to assess the extent of psoriasis. In PAS!, there are four parameters to be scored i.e., the surface area affected, erythema (redness), thickness and scaliness of the plaques. Determining PAS! score is a tedious task and thus it is not used in daily clinical practice. In addition, the PAS! parameters are visually determined and may result in intra-observer and inter-observer variations, even by experienced dermatologists. Objective methods in assessing area and erythema of psoriasis lesion have been developed in this thesis. Psoriasis lesion can be recognized by its colour dissimilarity with normal skin. Colour dissimilarity is represented by colour difference in CIELAB colour space, a widely used colour space to measure colour dissimilarity. Each pixel in CIELAB colour space can be represented by its lightness (L'), hue (hob), and chroma (Cab). Colour difference between psoriasis lesion and normal skin is analyzed in hue-chroma plane of CIELAB colour space. Centroids of normal skin and lesion in hue-chroma space are obtained from selected samples. Euclidean distances between all pixels with these two centroids are then calculated. Each pixel is assigned to the class of the nearest centroid. The erythema of psoriasis lesion is affected by degree of severity and skin pigmentation. In order to assess the erythema objectively, patients are grouped according to their skin pigmentation level. The L* value of normal skin which represents skin pigmentation level is utilized to group the patient into the three skin types namely fair, brown and dark skin types. Light difference (t.L*), hue difference (t.hab), and chroma difference (t.C'ab) of CIELAB colour space between reference lesions and the surrounding normal skin are analyzed. It is found that the erythema score of a lesion can be determined by their hue difference (t.hab) value within a particular skin type group. Out of 30 body regions, the proposed method is able to give the same PAS! area score as reference for 28 body regions. The proposed method is able to determine PAS! erythema score of 82 lesions obtained from 22 patients objectively without being influenced by other characteristic of the lesion such as area, pattern, and boundary

Mesh-to-raster based non-rigid registration of multi-modal images

Region of interest (ROI) alignment in medical images plays a crucial role in diagnostics, procedure planning, treatment, and follow-up. Frequently, a model is represented as triangulated mesh while the patient data is provided from CAT scanners as pixel or voxel data. Previously, we presented a 2D method for curve-to-pixel registration. This paper contributes (i) a general mesh-to-raster (M2R) framework to register ROIs in multi-modal images; (ii) a 3D surface-to-voxel application, and (iii) a comprehensive quantitative evaluation in 2D using ground truth provided by the simultaneous truth and performance level estimation (STAPLE) method. The registration is formulated as a minimization problem where the objective consists of a data term, which involves the signed distance function of the ROI from the reference image, and a higher order elastic regularizer for the deformation. The evaluation is based on quantitative light-induced fluoroscopy (QLF) and digital photography (DP) of decalcified teeth. STAPLE is computed on 150 image pairs from 32 subjects, each showing one corresponding tooth in both modalities. The ROI in each image is manually marked by three experts (900 curves in total). In the QLF-DP setting, our approach significantly outperforms the mutual information-based registration algorithm implemented with the Insight Segmentation and Registration Toolkit (ITK) and Elastix

Application of deep learning general-purpose neural architectures based on vision transformers for ISIC melanoma classification

The field of computer vision has for years been dominated by Convolutional Neural Networks (CNNs) in the medical field. However, there are various other Deep Learning (DL) techniques that have become very popular in this space. Vision Transformers (ViTs) are an example of a deep learning technique that has been gaining in popularity in recent years. In this work, we study the performance of ViTs and CNNs on skin lesions classification tasks, specifically melanoma diagnosis. We compare the performance of ViTs to that of CNNs and show that regardless of the performance of both architectures, an ensemble of the two can improve generalization. We also present an adaptation to the Gram-OOD* method (detecting Out-of-distribution (OOD) using Gram matrices) for skin lesion images. A rescaling method was also used to address the imbalanced dataset problem, which is generally inherent in medical images. The phenomenon of super-convergence was critical to our success in building models with computing and training time constraints. Finally, we train and evaluate an ensemble of ViTs and CNNs, demonstrating that generalization is enhanced by placing first in the 2019 and third in the 2022 ISIC Challenge Live. Leaderboard (available at \href{https://challenge.isic-archive.com/leaderboards/live/}{https://challenge.isic-archive.com/leaderboards/live/})

Noninvasive Thrombolysis Using Histotripsy Pulsed Ultrasound Cavitation Therapy.

Histotripsy is a noninvasive ultrasound therapy that utilizes short, high-amplitude, focused ultrasound pulses to mechanically reduce targeted tissue structures to liquid debris by acoustic cavitation. In this work, the physical mechanisms of histotripsy and its application as a method of thrombolysis were investigated. Cavitation activity which causes tissue breakdown during histotripsy was studied by high-speed photography. It was found that cavitation clouds form due to scattering of shock waves in a focused ultrasound pulse from individual inertial cavitation bubbles. The scattered shock is a large tensile wave which expands clusters of cavitation bubbles when the tensile pressure is greater than a measured threshold of approximately 30 MPa. The interaction of this cavitation with tissue and cells was explored with a phantom containing agarose and red blood cells to measure cavitation-based mechanical damage. The observations indicated that cell lysis may be achieved by bubble-induced tensile strain upon expansion, causing membrane rupture. Based on these studies, focused histotripsy therapy transducers were designed to controllably generate cavitation clouds in the vasculature for performing thrombolysis. Transducers were integrated with ultrasound imagers to provide feedback for targeting and monitoring progress of treatment. Rapid thrombolysis was observed when histotripsy was applied to clots in-vitro, and the resulting debris was mainly subcellular and unlikely to cause embolism. Additionally, it was observed that histotripsy can attract, trap, and destroy free clot fragments in a vessel phantom. Based on these observations, a noninvasive embolus trap (NET) was developed, acting as a filter to prevent embolism during the thrombolysis procedure. An in-vivo porcine model of deep-vein thrombosis was used to evaluate the safety and efficacy of the histotripsy thrombolysis technique. These experiments demonstrated the feasibility of the treatment and suggest histotripsy can achieve rapid clot breakdown in a controlled manner.Ph.D.Biomedical EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/91496/1/adamdm_1.pd

Development of a new spectral imaging system for the diagnosis of skin cancer

The incidence of skin cancer in Europe, US and Australia has been rising rapidly. Skin cancer accounts for one in three cancers worldwide and a person has 1:25 chance to develop a melanoma, the most aggressive form. Visual inspection followed by histological examination is, still today, the gold standard for clinicians, which is carried out through a dermoscope, a handheld device with a magnifying lens and a white and uniform illumination field. The dermoscopic technique requires considerable training in the interpretation of what is seen and is highly dependent on subjective impressions. In consequence, a large number of unnecessary surgical procedures are performed. For this reason, in this thesis a spectral imaging system to improve skin cancer diagnosis has been developed. This work has been carried out in the framework of the European project DIAGNOPTICS "Diagnosis of skin cancer using optics", which aimed to launch a hospital service based on a multiphotonic platform to improve skin cancer with the combination of four non-invasive novel techniques: 3D and multispectral imaging, optical feedback interferometry and confocal microscopy. The handheld system built included a monochromatic CCD camera attached to an objective lens and a light source containing 32 light emitting diodes (LEDs) with 8 spectral bands from 400 nm to 1000 nm. An acquisition software to control all the components of the multispectral system was programmed as well as a simplest version for physicians. The changes over time of the emission of the LEDs was analysed, and also the linear response of the camera at each wavelength, the uniformity of the LED emission and the short and long-term repeatability of the system in acquiring images, to ensure the good performance of the system. In order to proceed with the Ethical Committee approval and to launch the systems in both hospitals, irradiance and radiance measurements were done according to the standard UNE-EN 62471. A Graphical User Interface (GUI) was developed for the spectral images processing and corresponding analysis, allowing spectral and colorimetric features to be computed in terms of reflectance, absorbance and colour parameters. Furthermore, a segmentation algorithm was also implemented to extract the isolated information from the lesion. For all images calculated in terms of any of the parameters, conventional statistical descriptors were obtained. As a first approach to extracting textural information we also used the analysis of the statistical properties of the histogram. An inclusion criteria and a measurement protocol were established. From all lesions analysed, 620 were measured with the multispectral system, 572 of them had a clinical or histopathological diagnosis, and 502 could be properly segmented. Therefore, 429 skin lesions were finally included in the study: 290 nevi, 95 melanomas and 44 basal cell carcinomas. A classification algorithm was developed in order to decide whether the lesions were malignant (melanomas and basal cell carcinomas) or not (nevi), splitting previously the data into training and validations set of the same size. 15 parameters from 1309 were found to be not redundant providing a 91.3% of sensitivity and 54.5% of specificity. Accordingly, the addition of textural information was shown to be useful for the diagnosis of malignant lesions than the sole use of averaged spectral and colour information. The same steps were carried out for the 3D imaging system also included in the multiphotonic platform. In this case, 3 parameters were found to be useful for the classification providing values of 55.6% and 83.7% of sensitivity and specificity, respectively. Finally, the combination of both system was also tested as a first attempt to improve the detection of melanomas, providing 100% and 72.2% of sensitivity and specificity, respectively. However, the conclusions reached in this case should be taken with caution due to the limited number of lesions.La incidència del càncer de pell a Europa, Estats Units i Austràlia ha anat augmentant ràpidament. Representa un de cada tres càncers a tot el món i una persona té 1:25 oportunitats de desenvolupar un melanoma, la forma més agressiva. Actualment, la inspecció visual amb un dermoscopi seguida d'un examen histològic és l'estàndard utilitzat pels metges a l'hora de diagnosticar-lo. La dermoscòpia requereix una formació considerable per interpretar el que es veu i depèn de les impressions subjectives dels clínics. En conseqüència, es realitzen una gran quantitat de procediments quirúrgics innecessaris. Per aquest motiu, en aquesta tesi s'ha desenvolupat una sistema d'imatge espectral per millorar el diagnòstic del càncer de pell. Aquest treball s'ha realitzat dins el marc del projecte Europeu DIAGNOPTICS ¿Diagnosis del càncer de pell utilitzant òptica?, el qual ha posat a punt un servei hospitalari basat en un plataforma multifotònica que combina quatre tècniques òptiques innovadores: sistemes d'imatge multiespectral 3D, interferometria de retroalimentació i microscòpia confocal. El sistema portàtil desenvolupat inclou una càmera monocromàtica CCD, un objectiu i una font de llum formada per 32 díodes emissors de llum (LED) amb 8 bandes espectrals diferents que emeten des de 400 nm fins a 1000 nm. S'ha preparat un programa d'adquisició per controlar tots els components del sistema així com una versió més simple del mateix pels metges. Per tal d'assegurar el bon funcionament del sistema, es van analitzar els canvis temporals en l'emissió dels LEDs així com la seva uniformitat d'emissió, la resposta lineal de la càmera per cada longitud d'ona i la repetibilitat del sistema pel què fa a l'adquisició d'imatges. Per tal d'obtenir l'aprovació del Comitè Ètic i poder realitzar l'estudi clínic en els hospitals, es van dur a terme mesures d'irradiància i radiància d'acord amb la norma UNE-EN 62471. També es va implementar una interfície gràfica d'usuari (GUI) per al processament de les imatges espectrals i la seva corresponent anàlisi. Aquest algorisme permet calcular paràmetres espectrals i colorimètrics de la pell en termes de reflectància, absorbància i d'altres basats en el color. A més, inclús es va desenvolupar un algorisme de segmentació per extreure informació aïllada de cada lesió. Per a totes les imatges calculades en termes de qualsevol paràmetre, es van obtenir descriptors estadístics convencionals i també es van utilitzar propietats estadístiques dels histogrames com una primera aproximació d'extreure informació de textura de la lesió. Finalment, es van establir els criteris d'inclusió i un protocol de mesura. De totes les lesions analitzades, se'n van mesurar 620, de les quals 572 tenien un diagnòstic clínic o histopatològic; 502 es van poder segmentar adequadament. D'aquesta manera es van incloure 429 lesions cutànies a l'estudi: 290 nevus, 95 melanomes i 44 carcinomes de cèl·lules basals. Es va desenvolupar un algorisme de classificació per determinar si les lesions eren malignes (melanomes i carcinomes de cèl·lules basals) o no (nevus), dividint prèviament les dades en un grup d'entrenament i un altre de validació de la mateixa mida. Es va observar que 15 del 1309 paràmetres proporcionaven informació rellevant per a la classificació, obtenint una sensibilitat del 91,3% i una especificitat del 54,5%. Així doncs, es demostra que la incorporació d'informació de textura és molt útil per al diagnòstic del càncer de pell més enllà de la informació espectral i de color. Aquests mateixos passos es van seguir pel sistema 3D també inclòs en la plataforma multifotònica, tot i que en aquest cas el número de lesions de què es disposava era més limitat. En aquest cas, es van seleccionar 3 paràmetres i es va obtenir una sensibilitat del 55,6% i una especificitat del 83,7%. Finalment, amb la combinació d'ambdós sistemes la sensibilitat obtinguda va ser de100% i l'especificitat del 72,2%.Postprint (published version

Development of a new spectral imaging system for the diagnosis of skin cancer

The incidence of skin cancer in Europe, US and Australia has been rising rapidly. Skin cancer accounts for one in three cancers worldwide and a person has 1:25 chance to develop a melanoma, the most aggressive form. Visual inspection followed by histological examination is, still today, the gold standard for clinicians, which is carried out through a dermoscope, a handheld device with a magnifying lens and a white and uniform illumination field. The dermoscopic technique requires considerable training in the interpretation of what is seen and is highly dependent on subjective impressions. In consequence, a large number of unnecessary surgical procedures are performed. For this reason, in this thesis a spectral imaging system to improve skin cancer diagnosis has been developed. This work has been carried out in the framework of the European project DIAGNOPTICS "Diagnosis of skin cancer using optics", which aimed to launch a hospital service based on a multiphotonic platform to improve skin cancer with the combination of four non-invasive novel techniques: 3D and multispectral imaging, optical feedback interferometry and confocal microscopy. The handheld system built included a monochromatic CCD camera attached to an objective lens and a light source containing 32 light emitting diodes (LEDs) with 8 spectral bands from 400 nm to 1000 nm. An acquisition software to control all the components of the multispectral system was programmed as well as a simplest version for physicians. The changes over time of the emission of the LEDs was analysed, and also the linear response of the camera at each wavelength, the uniformity of the LED emission and the short and long-term repeatability of the system in acquiring images, to ensure the good performance of the system. In order to proceed with the Ethical Committee approval and to launch the systems in both hospitals, irradiance and radiance measurements were done according to the standard UNE-EN 62471. A Graphical User Interface (GUI) was developed for the spectral images processing and corresponding analysis, allowing spectral and colorimetric features to be computed in terms of reflectance, absorbance and colour parameters. Furthermore, a segmentation algorithm was also implemented to extract the isolated information from the lesion. For all images calculated in terms of any of the parameters, conventional statistical descriptors were obtained. As a first approach to extracting textural information we also used the analysis of the statistical properties of the histogram. An inclusion criteria and a measurement protocol were established. From all lesions analysed, 620 were measured with the multispectral system, 572 of them had a clinical or histopathological diagnosis, and 502 could be properly segmented. Therefore, 429 skin lesions were finally included in the study: 290 nevi, 95 melanomas and 44 basal cell carcinomas. A classification algorithm was developed in order to decide whether the lesions were malignant (melanomas and basal cell carcinomas) or not (nevi), splitting previously the data into training and validations set of the same size. 15 parameters from 1309 were found to be not redundant providing a 91.3% of sensitivity and 54.5% of specificity. Accordingly, the addition of textural information was shown to be useful for the diagnosis of malignant lesions than the sole use of averaged spectral and colour information. The same steps were carried out for the 3D imaging system also included in the multiphotonic platform. In this case, 3 parameters were found to be useful for the classification providing values of 55.6% and 83.7% of sensitivity and specificity, respectively. Finally, the combination of both system was also tested as a first attempt to improve the detection of melanomas, providing 100% and 72.2% of sensitivity and specificity, respectively. However, the conclusions reached in this case should be taken with caution due to the limited number of lesions.La incidència del càncer de pell a Europa, Estats Units i Austràlia ha anat augmentant ràpidament. Representa un de cada tres càncers a tot el món i una persona té 1:25 oportunitats de desenvolupar un melanoma, la forma més agressiva. Actualment, la inspecció visual amb un dermoscopi seguida d'un examen histològic és l'estàndard utilitzat pels metges a l'hora de diagnosticar-lo. La dermoscòpia requereix una formació considerable per interpretar el que es veu i depèn de les impressions subjectives dels clínics. En conseqüència, es realitzen una gran quantitat de procediments quirúrgics innecessaris. Per aquest motiu, en aquesta tesi s'ha desenvolupat una sistema d'imatge espectral per millorar el diagnòstic del càncer de pell. Aquest treball s'ha realitzat dins el marc del projecte Europeu DIAGNOPTICS ¿Diagnosis del càncer de pell utilitzant òptica?, el qual ha posat a punt un servei hospitalari basat en un plataforma multifotònica que combina quatre tècniques òptiques innovadores: sistemes d'imatge multiespectral 3D, interferometria de retroalimentació i microscòpia confocal. El sistema portàtil desenvolupat inclou una càmera monocromàtica CCD, un objectiu i una font de llum formada per 32 díodes emissors de llum (LED) amb 8 bandes espectrals diferents que emeten des de 400 nm fins a 1000 nm. S'ha preparat un programa d'adquisició per controlar tots els components del sistema així com una versió més simple del mateix pels metges. Per tal d'assegurar el bon funcionament del sistema, es van analitzar els canvis temporals en l'emissió dels LEDs així com la seva uniformitat d'emissió, la resposta lineal de la càmera per cada longitud d'ona i la repetibilitat del sistema pel què fa a l'adquisició d'imatges. Per tal d'obtenir l'aprovació del Comitè Ètic i poder realitzar l'estudi clínic en els hospitals, es van dur a terme mesures d'irradiància i radiància d'acord amb la norma UNE-EN 62471. També es va implementar una interfície gràfica d'usuari (GUI) per al processament de les imatges espectrals i la seva corresponent anàlisi. Aquest algorisme permet calcular paràmetres espectrals i colorimètrics de la pell en termes de reflectància, absorbància i d'altres basats en el color. A més, inclús es va desenvolupar un algorisme de segmentació per extreure informació aïllada de cada lesió. Per a totes les imatges calculades en termes de qualsevol paràmetre, es van obtenir descriptors estadístics convencionals i també es van utilitzar propietats estadístiques dels histogrames com una primera aproximació d'extreure informació de textura de la lesió. Finalment, es van establir els criteris d'inclusió i un protocol de mesura. De totes les lesions analitzades, se'n van mesurar 620, de les quals 572 tenien un diagnòstic clínic o histopatològic; 502 es van poder segmentar adequadament. D'aquesta manera es van incloure 429 lesions cutànies a l'estudi: 290 nevus, 95 melanomes i 44 carcinomes de cèl·lules basals. Es va desenvolupar un algorisme de classificació per determinar si les lesions eren malignes (melanomes i carcinomes de cèl·lules basals) o no (nevus), dividint prèviament les dades en un grup d'entrenament i un altre de validació de la mateixa mida. Es va observar que 15 del 1309 paràmetres proporcionaven informació rellevant per a la classificació, obtenint una sensibilitat del 91,3% i una especificitat del 54,5%. Així doncs, es demostra que la incorporació d'informació de textura és molt útil per al diagnòstic del càncer de pell més enllà de la informació espectral i de color. Aquests mateixos passos es van seguir pel sistema 3D també inclòs en la plataforma multifotònica, tot i que en aquest cas el número de lesions de què es disposava era més limitat. En aquest cas, es van seleccionar 3 paràmetres i es va obtenir una sensibilitat del 55,6% i una especificitat del 83,7%. Finalment, amb la combinació d'ambdós sistemes la sensibilitat obtinguda va ser de100% i l'especificitat del 72,2%