Machine Vision System for 3D Plant Phenotyping.

Machine vision for plant phenotyping is an emerging research area for producing high throughput in agriculture and crop science applications. Since 2D based approaches have their inherent limitations, 3D plant analysis is becoming state of the art for current phenotyping technologies. We present an automated system for analyzing plant growth in indoor conditions. A gantry robot system is used to perform scanning tasks in an automated manner throughout the lifetime of the plant. A 3D laser scanner mounted as the robot's payload captures the surface point cloud data of the plant from multiple views. The plant is monitored from the vegetative to reproductive stages in light/dark cycles inside a controllable growth chamber. An efficient 3D reconstruction algorithm is used, by which multiple scans are aligned together to obtain a 3D mesh of the plant, followed by surface area and volume computations. The whole system, including the programmable growth chamber, robot, scanner, data transfer, and analysis is fully automated in such a way that a naive user can, in theory, start the system with a mouse click and get back the growth analysis results at the end of the lifetime of the plant with no intermediate intervention. As evidence of its functionality, we show and analyze quantitative results of the rhythmic growth patterns of the dicot Arabidopsis thaliana (L.), and the monocot barley (Hordeum vulgare L.) plants under their diurnal light/dark cycles.

A proposed framework for consensus-based lung tumour volume auto-segmentation in 4D computed tomography imaging.

This work aims to propose and validate a framework for tumour volume auto-segmentation based on ground-truth estimates derived from multi-physician input contours to expedite 4D-CT based lung tumour volume delineation. 4D-CT datasets of ten non-small cell lung cancer (NSCLC) patients were manually segmented by 6 physicians. Multi-expert ground truth (GT) estimates were constructed using the STAPLE algorithm for the gross tumour volume (GTV) on all respiratory phases. Next, using a deformable model-based method, multi-expert GT on each individual phase of the 4D-CT dataset was propagated to all other phases providing auto-segmented GTVs and motion encompassing internal gross target volumes (IGTVs) based on GT estimates (STAPLE) from each respiratory phase of the 4D-CT dataset. Accuracy assessment of auto-segmentation employed graph cuts for 3D-shape reconstruction and point-set registration-based analysis yielding volumetric and distance-based measures. STAPLE-based auto-segmented GTV accuracy ranged from (81.51 ± 1.92) to (97.27 ± 0.28)% volumetric overlap of the estimated ground truth. IGTV auto-segmentation showed significantly improved accuracies with reduced variance for all patients ranging from 90.87 to 98.57% volumetric overlap of the ground truth volume. Additional metrics supported these observations with statistical significance. Accuracy of auto-segmentation was shown to be largely independent of selection of the initial propagation phase. IGTV construction based on auto-segmented GTVs within the 4D-CT dataset provided accurate and reliable target volumes compared to manual segmentation-based GT estimates. While inter-/intra-observer effects were largely mitigated, the proposed segmentation workflow is more complex than that of current clinical practice and requires further development.

Impact of target volume segmentation accuracy and variability on treatment planning for 4D-CT-based non-small cell lung cancer radiotherapy.

BACKGROUND: Accurate target volume segmentation is crucial for success in image-guided radiotherapy. However, variability in anatomical segmentation is one of the most significant contributors to uncertainty in radiotherapy treatment planning. This is especially true for lung cancer where target volumes are subject to varying magnitudes of respiratory motion. MATERIAL AND METHODS: This study aims to analyze multiple observer target volume segmentations and subsequent intensity-modulated radiotherapy (IMRT) treatment plans defined by those segmentations against a reference standard for lung cancer patients imaged with four-dimensional computed tomography (4D-CT). Target volume segmentations of 10 patients were performed manually by six physicians, allowing for the calculation of ground truth estimate segmentations via the simultaneous truth and performance level estimation (STAPLE) algorithm. Segmentation variability was assessed in terms of distance- and volume-based metrics. Treatment plans defined by these segmentations were then subject to dosimetric evaluation consisting of both physical and radiobiological analysis of optimized 3D dose distributions. RESULTS: Significant differences were noticed amongst observers in comparison to STAPLE segmentations and this variability directly extended into the treatment planning stages in the context of all dosimetric parameters used in this study. Mean primary tumor control probability (TCP) ranged from (22.6±11.9)% to (33.7±0.6)%, with standard deviation ranging from 0.5% to 11.9%. However, mean normal tissue complication probabilities (NTCP) based on treatment plans for each physician-derived target volume well as the NTCP derived from STAPLE-based treatment plans demonstrated no discernible trends and variability appeared to be patient-specific. This type of variability demonstrated the large-scale impact that target volume segmentation uncertainty can play in IMRT treatment planning. CONCLUSIONS: Significant target volume segmentation and dosimetric variability exists in IMRT treatment planning amongst experts in the presence of a reference standard for 4D-CT-based lung cancer radiotherapy. Future work is needed to mitigate this uncertainty and ensure highly accurate and effective radiotherapy for lung cancer patients.