I realised the common point between Cellpose and EmbedSeg is utilise a center point of an object though both methods use different methods (The former uses heat flows, the latter uses spatial embedding).
\nSo I hypothesized the key factor for instance segmentation of microscopy is detect a center of an object.
\nThen I surveyed if any other related papers.
\nHere are the short list I found.
Abstract
\nInstance segmentation of biological images is essential for studying object behaviors and properties. The challenges, such as clustering, occlusion, and adhesion problems of the objects, make instance segmentation a non-trivial task. Current box-free instance segmentation methods typically rely on local pixel-level information. Due to a lack of global object view, these methods are prone to over- or under-segmentation. On the contrary, the box-based instance segmentation methods incorporate object detection into the segmentation, performing better in identifying the individual instances. In this paper, we propose a new box-based instance segmentation method. Mainly, we locate the object bounding boxes from their center points. The object features are subsequently reused in the segmentation branch as a guide to separate the clustered instances within an RoI patch. Along with the instance normalization, the model is able to recover the target object distribution and suppress the distribution of neighboring attached objects. Consequently, the proposed model performs excellently in segmenting the clustered objects while retaining the target object details. The proposed method achieves state-of-the-art performances on three biological datasets: cell nuclei, plant phenotyping dataset, and neural cells.
This research uses CenterMask and is provided by researchers from Sartorius.
\nI can only access abstract becase this was provided by Poster Flash Talk + Poster.
Abstract
\nCombining high-throughput, live-cell imaging with accessible, non-invasive label-free modalities such as phase contrast imaging provides great spatiotemporal resolution to study biological phenomena. Accurate segmentation of individual cells enables exploration of complex biological questions. However, due to low contrast and high object density, this requires sophisticated imaging processing pipelines such as convolutional neural networks (CNNs). We previously reported on LIVECell, an open-source, high-quality, manually annotated and expert-validated dataset, comprising over 1.6 million annotated cells of 8 highly diverse cell types from initial seeding to full confluence (Edlund et al., in review). Alongside the dataset, we also trained instance segmentation CNN models with the CenterMask architecture (Lee and Park, 2020). Here, we fine-tune one of our publicly available LIVECell-trained models to enable quantitative analysis of complex morphological change associated with two applications, cell viability and differentiation. While these assays are commonly quantified using fluorescent or luminescent reporter reagents, such as cell death reporters or differentiation markers, the use of these reagents can require time-consuming optimisation steps or can perturb valuable samples. Taking the output label-free segmentation masks from our fine-tuned LIVECell models, we demonstrate the ability to quantify cell death and differentiation using the morphological features of phase contrast images without the use of reporter agents.
I realised the common point between Cellpose and EmbedSeg is utilise a center point of an object though both methods use different methods (The former uses heat flows, the latter uses spatial embedding).
\nSo I hypothesized the key factor for instance segmentation of microscopy is detect a center of an object.
\nThen I surveyed if any other related papers.
\nHere are the short list I found.
Abstract
\nInstance segmentation of biological images is essential for studying object behaviors and properties. The challenges, such as clustering, occlusion, and adhesion problems of the objects, make instance segmentation a non-trivial task. Current box-free instance segmentation methods typically rely on local pixel-level information. Due to a lack of global object view, these methods are prone to over- or under-segmentation. On the contrary, the box-based instance segmentation methods incorporate object detection into the segmentation, performing better in identifying the individual instances. In this paper, we propose a new box-based instance segmentation method. Mainly, we locate the object bounding boxes from their center points. The object features are subsequently reused in the segmentation branch as a guide to separate the clustered instances within an RoI patch. Along with the instance normalization, the model is able to recover the target object distribution and suppress the distribution of neighboring attached objects. Consequently, the proposed model performs excellently in segmenting the clustered objects while retaining the target object details. The proposed method achieves state-of-the-art performances on three biological datasets: cell nuclei, plant phenotyping dataset, and neural cells.
This research uses CenterMask and is provided by researchers from Sartorius.
\nI can only access abstract becase this was provided by Poster Flash Talk + Poster.
Abstract
\nCombining high-throughput, live-cell imaging with accessible, non-invasive label-free modalities such as phase contrast imaging provides great spatiotemporal resolution to study biological phenomena. Accurate segmentation of individual cells enables exploration of complex biological questions. However, due to low contrast and high object density, this requires sophisticated imaging processing pipelines such as convolutional neural networks (CNNs). We previously reported on LIVECell, an open-source, high-quality, manually annotated and expert-validated dataset, comprising over 1.6 million annotated cells of 8 highly diverse cell types from initial seeding to full confluence (Edlund et al., in review). Alongside the dataset, we also trained instance segmentation CNN models with the CenterMask architecture (Lee and Park, 2020). Here, we fine-tune one of our publicly available LIVECell-trained models to enable quantitative analysis of complex morphological change associated with two applications, cell viability and differentiation. While these assays are commonly quantified using fluorescent or luminescent reporter reagents, such as cell death reporters or differentiation markers, the use of these reagents can require time-consuming optimisation steps or can perturb valuable samples. Taking the output label-free segmentation masks from our fine-tuned LIVECell models, we demonstrate the ability to quantify cell death and differentiation using the morphological features of phase contrast images without the use of reporter agents.
I found the code from the author.
\nhttps://github.com/yijingru/ObjGuided-Instance-Segmentation
I found the code from the author.
\nhttps://github.com/yijingru/ObjGuided-Instance-Segmentation