7 proposed a pipeline that used maker-controlled watersheds followed by postprocessing steps to segment cell nuclei on haematoxylin and eosin (H&E) stained images. More commonly, combinations of methods are used for specific nuclear/cell segmentation tasks. These methods used alone are seldom able to achieve satisfactory results. non-deep learning), including thresholding, filtering, morphological operations, region accumulation, and model fitting. Meijering 6 published a comprehensive review of the literature on methodologies for nuclear/cell segmentation, covering many conventional algorithms (i.e. Accurate cell segmentation on the multiplexed immunofluorescence (MxIF) images is an essential step when generating profiling information for downstream analysis. Immunofluorescent multiplexing, one of several multiplexing technologies that have recently become available, allows the labeling of different protein markers with immunofluorescent (IF) conjugated antibodies on the same tissue section simultaneously. Advances in imaging and automatic analysis (including artificial intelligence) can dramatically impact the ability to perform such characterization 1. Simultaneously characterizing both immune and tumor-related pathways can empower a more accurate patient stratification for immunotherapy 3, 4, 5. This includes identifying and quantifying different immune cell subsets, their spatial arrangement, and the expression of immune checkpoint markers on these cells 2. Immuno-oncology profiling requires a detailed assessment of the tumor microenvironment 1.
#Cellprofiler intensity segmentation software#
We deployed the model as a plug-in to CellProfiler, a widely used software platform for cellular image analysis. Our results demonstrate that using two-stage domain adaptation with a weakly labeled dataset can effectively boost system performance, especially when using a small training sample size. When using smaller training sample sizes for fine-tuning, the proposed method provided comparable performance to that obtained using much larger training sample sizes. Our proposed method, using a weakly labeled dataset for pre-training, showed superior performance in all of our experiments. Our method yields comparable results to the multi-observer agreement on an ovarian cancer dataset and improves on state-of-the-art performance on a publicly available dataset of mouse pancreatic tissues.
#Cellprofiler intensity segmentation manual#
We validated our method against manual annotations on three different datasets. We used two-stage domain adaptation by first using a weakly labeled dataset followed by fine-tuning with a manually annotated dataset. We propose a deep learning pipeline to train a Mask R-CNN model (deep network) for cell segmentation using nuclear (DAPI) and membrane (Na +K +ATPase) stained images. Accurate cell segmentation of the MxIF images is an essential step. Cellular profiling with multiplexed immunofluorescence (MxIF) images can contribute to a more accurate patient stratification for immunotherapy.
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