Skin3D: Detection and longitudinal tracking of pigmented skin lesions in 3D total-body textured meshes

We present an automated approach to detect and longitudinally track skin lesions on 3D total-body skin surface scans. The acquired 3D mesh of the subject is unwrapped to a 2D texture image, where a trained objected detection model, Faster R-CNN, localizes the lesions within the 2D domain. These detected skin lesions are mapped back to the 3D surface of the subject and, for subjects imaged multiple times, we construct a graph-based matching procedure to longitudinally track lesions that considers the anatomical correspondences among pairs of meshes and the geodesic proximity of corresponding lesions and the inter-lesion geodesic distances.We evaluated the proposed approach using 3DBodyTex, a publicly available dataset composed of 3D scans imaging the coloured skin (textured meshes) of 200 human subjects. We manually annotated locations that appeared to the human eye to contain a pigmented skin lesion as well as tracked a subset of lesions occurring on the same subject imaged in different poses. Our results, when compared to three human annotators, suggest that the trained Faster R-CNN detects lesions at a similar performance level as the human annotators. Our lesion tracking algorithm achieves an average matching accuracy of 88% on a set of detected corresponding pairs of prominent lesions of subjects imaged in different poses, and an average longitudinal accuracy of 71% when encompassing additional errors due to lesion detection. As there currently is no other large-scale publicly available dataset of 3D total-body skin lesions, we publicly release over 25,000 3DBodyTex manual annotations, which we hope will further research on total-body skin lesion analysis.     

Alteration of lumbar muscle morphology and composition in relation to low back pain: a systematic review and meta-analysis

BACKGROUND CONTEXTPrevious studies have proposed that there is a relationship between low back pain (LBP) and morphology and composition of paraspinal muscles. However, results have been conflicting, especially regarding fatty infiltration of muscles.PURPOSEThe primary goal of this study was to review and analyze results from imaging studies which investigated morphological and composition changes in the multifidus, erector spinae and psoas major muscles in people with LBP.STUDY DESIGN/SETTINGSystematic review with meta-analysis.PATIENT SAMPLEA patient sample was not requiredOUTCOME MEASURESThis review did not have outcome measures.METHODSPubMed, Scopus, Web of Sciences, EMBASE and ProQuest were searched for eligible studies up to 31st July 2020 (all languages). A systematic search of electronic databases was conducted to identify studies investigating the association between the morphology and fat content of lumbar muscles in people with LBP compared with a (no LBP) control group. 13,795 articles were identified. Based on the screening for inclusion/ exclusion, 25 were included. The quality of the studies was evaluated using the Newcastle-Ottawa Scale. From the 25 articles, 20 were included in the meta-analysis.RESULTSResults showed that the total cross-sectional area of the multifidus was smaller in people with LBP (Standardized mean difference, SMD = -0.24, 95% CI = -0.5 to 0.03). Combined SMDs showed a medium effect of LBP on increasing multifidus muscle fat infiltration (SMD = 0.61, 95% CI = 0.30 to 0.91). There were no LBP related differences identified in the morphology or composition of the lumbar erector spine and psoas major muscles.CONCLUSIONSPeople with LBP were found to have somewhat smaller multifidus muscles with a significant amount of intramuscular fat infiltration. Varying sample size, age and BMI of participants, quality of studies and the procedures used to measure fat infiltration are possible reasons for inconsistencies in results of previous studies.     

Deep learning with biopsy whole slide images for pretreatment prediction of pathological complete response to neoadjuvant chemotherapy in breast cancer：A multicenter study

IntroductionPredicting pathological complete response (pCR) for patients receiving neoadjuvant chemotherapy (NAC) is crucial in establishing individualized treatment. Whole-slide images (WSIs) of tumor tissues reflect the histopathologic information of the tumor, which is important for therapeutic response effectiveness. In this study, we aimed to investigate whether predictive information for pCR could be detected from WSIs.Materials and methodsWe retrospectively collected data from four cohorts of 874 patients diagnosed with biopsy-proven breast cancer. A deep learning pathological model (DLPM) was constructed to predict pCR using biopsy WSIs in the primary cohort, and it was then validated in three external cohorts. The DLPM could generate a deep learning pathological score (DLPs) for each patient; stromal tumor-infiltrating lymphocytes (TILs) were selected for comparison with DLPs.ResultsThe WSI feature-based DLPM showed good predictive performance with the highest area under the curve (AUC) of 0.72 among the cohorts. Alternatively, the combination of the DLPM and clinical characteristics offered a better prediction performance (AUC >0.70) in all cohorts. We also evaluated the performance of DLPM in three different breast subtypes with the best prediction for the triple-negative breast cancer (TNBC) subtype (AUC: 0.73). Moreover, DLPM combined with clinical characteristics and stromal TILs achieved the highest AUC in the primary cohort (AUC: 0.82) and validation cohort 1 (AUC: 0.80).ConclusionOur study suggested that WSIs integrated with deep learning could potentially predict pCR to NAC in breast cancer. The predictive performance will be improved by combining clinical characteristics. DLPs from DLPM can provide more information compared to stromal TILs for pCR prediction.     

Super-resolution Ultrasound Imaging

The majority of exchanges of oxygen and nutrients are performed around vessels smaller than 100 μm, allowing cells to thrive everywhere in the body. Pathologies such as cancer, diabetes and arteriosclerosis can profoundly alter the microvasculature. Unfortunately, medical imaging modalities only provide indirect observation at this scale. Inspired by optical microscopy, ultrasound localization microscopy has bypassed the classic compromise between penetration and resolution in ultrasonic imaging. By localization of individual injected microbubbles and tracking of their displacement with a subwavelength resolution, vascular and velocity maps can be produced at the scale of the micrometer. Super-resolution ultrasound has also been performed through signal fluctuations with the same type of contrast agents, or through switching on and off nano-sized phase-change contrast agents. These techniques are now being applied pre-clinically and clinically for imaging of the microvasculature of the brain, kidney, skin, tumors and lymph nodes.