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

Evaluating and Improving 4D-CT Image Segmentation for Lung Cancer Radiotherapy

Lung cancer is a high-incidence disease with low survival despite surgical advances and concurrent chemo-radiotherapy strategies. Image-guided radiotherapy provides for treatment measures, however, significant challenges exist for imaging, treatment planning, and delivery of radiation due to the influence of respiratory motion. 4D-CT imaging is capable of improving image quality of thoracic target volumes influenced by respiratory motion. 4D-CT-based treatment planning strategies requires highly accurate anatomical segmentation of tumour volumes for radiotherapy treatment plan optimization. Variable segmentation of tumour volumes significantly contributes to uncertainty in radiotherapy planning due to a lack of knowledge regarding the exact shape of the lesion and difficulty in quantifying variability. As image-segmentation is one of the earliest tasks in the radiotherapy process, inherent geometric uncertainties affect subsequent stages, potentially jeopardizing patient outcomes. Thus, this work assesses and suggests strategies for mitigation of segmentation-related geometric uncertainties in 4D-CT-based lung cancer radiotherapy at pre- and post-treatment planning stages

Combining crowd worker, algorithm, and expert efforts to find boundaries of objects in images

While traditional approaches to image analysis have typically relied upon either manual annotation by experts or purely-algorithmic approaches, the rise of crowdsourcing now provides a new source of human labor to create training data or perform computations at run-time. Given this richer design space, how should we utilize algorithms, crowds, and experts to better annotate images? To answer this question for the important task of finding the boundaries of objects or regions in images, I focus on image segmentation, an important precursor to solving a variety of fundamental image analysis problems, including recognition, classification, tracking, registration, retrieval, and 3D visualization. The first part of the work includes a detailed analysis of the relative strengths and weaknesses of three different approaches to demarcate object boundaries in images: by experts, by crowdsourced laymen, and by automated computer vision algorithms. The second part of the work describes three hybrid system designs that integrate computer vision algorithms and crowdsourced laymen to demarcate boundaries in images. Experiments revealed that hybrid system designs yielded more accurate results than relying on algorithms or crowd workers alone and could yield segmentations that are indistinguishable from those created by biomedical experts. To encourage community-wide effort to continue working on developing methods and systems for image-based studies which can have real and measurable impact that benefit society at large, datasets and code are publicly-shared (http://www.cs.bu.edu/~betke/BiomedicalImageSegmentation/)

Combining crowd worker, algorithm, and expert efforts to find boundaries of objects in images

While traditional approaches to image analysis have typically relied upon either manual annotation by experts or purely-algorithmic approaches, the rise of crowdsourcing now provides a new source of human labor to create training data or perform computations at run-time. Given this richer design space, how should we utilize algorithms, crowds, and experts to better annotate images? To answer this question for the important task of finding the boundaries of objects or regions in images, I focus on image segmentation, an important precursor to solving a variety of fundamental image analysis problems, including recognition, classification, tracking, registration, retrieval, and 3D visualization. The first part of the work includes a detailed analysis of the relative strengths and weaknesses of three different approaches to demarcate object boundaries in images: by experts, by crowdsourced laymen, and by automated computer vision algorithms. The second part of the work describes three hybrid system designs that integrate computer vision algorithms and crowdsourced laymen to demarcate boundaries in images. Experiments revealed that hybrid system designs yielded more accurate results than relying on algorithms or crowd workers alone and could yield segmentations that are indistinguishable from those created by biomedical experts. To encourage community-wide effort to continue working on developing methods and systems for image-based studies which can have real and measurable impact that benefit society at large, datasets and code are publicly-shared (http://www.cs.bu.edu/~betke/BiomedicalImageSegmentation/)