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This literature review assesses the efficacy of image-processing techniques and machine-learning models in computer vision for wood log grading and scaling. Four searches were conducted in four scientific databases, yielding a total of 1288 results, which were narrowed down to 33 relevant studies. The studies were categorized according to their goals, including log end grading, log side grading, individual log scaling, log pile scaling, and log segmentation. The studies were compared based on the input used, choice of model, model performance, and level of autonomy. This review found a preference for images over point cloud representations for logs and an increase in camera use over laser scanners. It identified three primary model types: classical image-processing algorithms, deep learning models, and other machine learning models. However, comparing performance across studies proved challenging due to varying goals and metrics. Deep learning models showed better performance in the log pile scaling and log segmentation goal categories. Cameras were found to have become more popular over time compared to laser scanners, possibly due to stereovision cameras taking over for laser scanners for sampling point cloud datasets. Classical image-processing algorithms were consistently used, deep learning models gained prominence in 2018, and other machine learning models were used in studies published between 2010 and 2018. [ABSTRACT FROM AUTHOR]

Tumor delineation is time- and labor-intensive and prone to inter- and intraobserver variations. Magnetic resonance imaging (MRI) provides good soft tissue contrast, and functional MRI captures tissue properties that may be valuable for tumor delineation. We explored MRI-based automatic segmentation of rectal cancer using a deep learning (DL) approach. We first investigated potential improvements when including both anatomical T2-weighted (T2w) MRI and diffusion-weighted MR images (DWI). Secondly, we investigated generalizability by including a second, independent cohort. Two cohorts of rectal cancer patients (C1 and C2) from different hospitals with 109 and 83 patients, respectively, were subject to 1.5 T MRI at baseline. T2w images were acquired for both cohorts and DWI (b-value of 500 s/mm2) for patients in C1. Tumors were manually delineated by three radiologists (two in C1, one in C2). A 2D U-Net was trained on T2w and T2w + DWI. Optimal parameters for image pre-processing and training were identified on C1 using five-fold cross-validation and patient Dice similarity coefficient (DSCp) as performance measure. The optimized models were evaluated on a C1 hold-out test set and the generalizability was investigated using C2. For cohort C1, the T2w model resulted in a median DSCp of 0.77 on the test set. Inclusion of DWI did not further improve the performance (DSCp 0.76). The T2w-based model trained on C1 and applied to C2 achieved a DSCp of 0.59. T2w MR-based DL models demonstrated high performance for automatic tumor segmentation, at the same level as published data on interobserver variation. DWI did not improve results further. Using DL models on unseen cohorts requires caution, and one cannot expect the same performance. [ABSTRACT FROM AUTHOR]

Accurate target volume delineation is a prerequisite for high-precision radiotherapy. However, manual delineation is resource-demanding and prone to interobserver variation. An automatic delineation approach could potentially save time and increase delineation consistency. In this study, the applicability of deep learning for fully automatic delineation of the gross tumour volume (GTV) in patients with anal squamous cell carcinoma (ASCC) was evaluated for the first time. An extensive comparison of the effects single modality and multimodality combinations of computed tomography (CT), positron emission tomography (PET), and magnetic resonance imaging (MRI) have on automatic delineation quality was conducted. 18F-fluorodeoxyglucose PET/CT and contrast-enhanced CT (ceCT) images were collected for 86 patients with ASCC. A subset of 36 patients also underwent a study-specific 3T MRI examination including T2- and diffusion-weighted imaging. The resulting two datasets were analysed separately. A two-dimensional U-Net convolutional neural network (CNN) was trained to delineate the GTV in axial image slices based on single or multimodality image input. Manual GTV delineations constituted the ground truth for CNN model training and evaluation. Models were evaluated using the Dice similarity coefficient (Dice) and surface distance metrics computed from five-fold cross-validation. CNN-generated automatic delineations demonstrated good agreement with the ground truth, resulting in mean Dice scores of 0.65–0.76 and 0.74–0.83 for the 86 and 36-patient datasets, respectively. For both datasets, the highest mean Dice scores were obtained using a multimodal combination of PET and ceCT (0.76–0.83). However, models based on single modality ceCT performed comparably well (0.74–0.81). T2W-only models performed acceptably but were somewhat inferior to the PET/ceCT and ceCT-based models. CNNs provided high-quality automatic GTV delineations for both single and multimodality image input, indicating that deep learning may prove a versatile tool for target volume delineation in future patients with ASCC. [ABSTRACT FROM AUTHOR]

Purpose: Non-invasive response monitoring can potentially be used to guide therapy selection for breast cancer patients. We employed dynamic 2-deoxy-2-[18F]fluoro-D-glucose positron emission tomography ([18F]FDG PET) to evaluate changes in three breast cancer xenograft lines in mice following three chemotherapy regimens.Procedures: Sixty-six athymic nude mice bearing bilateral breast cancer xenografts (two basal-like and one luminal-like subtype) underwent three 60 min [18F]FDG PET scans. Scans were performed prior to and 3 and 10 days after treatment with doxorubicin, paclitaxel, or carboplatin. Tumor growth was monitored in parallel. A pharmacokinetic compartmental model was fitted to the tumor uptake curves, providing estimates of transfer rates between the vascular, non-metabolized, and metabolized compartments. Early and late standardized uptake values (SUVE and SUVL, respectively); the rate constants k 1, k 2, and k 3, and the intravascular fraction v B were estimated. Changes in tumor volume were used as a response measure. Multivariate partial least-squares regression (PLSR) was used to assess if PET parameters could model tumor response and to identify PET parameters with the largest impact on response.Results: Treatment responders had significantly larger perfusion-related parameters (k 1 and k 2) and lower metabolism-related parameter (k 3) than non-responders 10 days after the start of treatment. These findings were further supported by the PLSR analysis, which showed that k 1 and k 2 at day 10 and changes in k 3 explained most of the variability in response to therapy, whereas SUVL and particularly SUVE were of lesser importance.Conclusions: Overall, rate parameters related to both tumor perfusion and metabolism were associated with tumor response. Conventional metrics of [18F]FDG uptake such as SUVE and SUVL apparently had little relation to tumor response, thus necessitating full dynamic scanning and pharmacokinetic analysis for optimal evaluation of chemotherapy-induced changes in breast cancers. [ABSTRACT FROM AUTHOR]

Long photoperiods characteristic of summers at high latitudes can increase ozone-induced foliar injury in subterranean clover (Trifolium subterraneum). This study compared the effects of long photoperiods on ozone injury in red and white clover cultivars adapted to shorter or longer daylengths of southern or northern Fennoscandia. Plants were exposed to 70 ppb ozone for six hours during the daytime for three consecutive days. Simultaneously, the daylength in the growth rooms was altered to long-day (10 h light; 14 h dim light) and short-day (10 h light; 14 h darkness) conditions. Thermal imaging showed that ozone disrupted leaf temperature and stomatal function, particularly in sensitive species, in which leaf temperature deviations persisted for several days after ozone exposure. Long-day conditions increased visible foliar injury (30%-70%), characterized by chlorotic and necrotic areas, relative to short day conditions in all species and cultivars independently of the photoperiod in the region they were adapted to. [ABSTRACT FROM AUTHOR]