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Examples of histopathology images for different organs (rows) showing various nuclear appearances. What makes this task even more challenging is that test images from the bladder, colon, and stomach are not represented in the training set. Our method is trained with only 25% of the full annotation.
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Quantitative analysis of cell nuclei in microscopic images is an essential yet challenging source of biological and pathological information. The major challenge is accurate detection and segmentation of densely packed nuclei in images acquired under a variety of conditions. Mask R-CNN-based methods have achieved state-of-the-art nucleus segmentati...
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... histopathology datasets presented in [33] and [40]. The first dataset consists of 30 annotated histology images of patients from several hospitals [40]. These images come from 7 different organs and represent different cancer types. An illustration of the variability of tissue appearances and their nuclei observed in this dataset is shown in Fig. 5. We use exactly the same subset of images (14 images) for testing as used in [40] and [33] to benchmark against their methods. After randomly removing 75% of the annotations from the 16 training images, we trained our network for 2000 iterations with a batch size of 4. Table 3 shows the overall performances of the different methods as ...
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... To improve image quality and clarity, the pipeline incorporates low-level techniques such as denoising and superresolution, enabling the visualization of finer structural details [57][58][59][60][61][62][71][72][73][74][75][76][77]. Advanced procedures, including cell segmentation and neuronal morphology reconstruction [91][92][93][94][95][96][97][98][99][100][101][102][103][104][105][106][107][108][109][110], are also employed to comprehend the intricate connectivity and dynamics of neural circuits at the mesoscopic scale. The ultimate goal of these image processing steps is to accurately map and analyze neural circuits, providing insights into the complex networks of connectivity and interactions that underpin various brain functions (Fig. 1a). ...
... (d) Noisy images are processed using denoising algorithm to remove unwanted artifacts that can arise from numerous factors [65]. (e) Cell segmentation using CNN allow automatic detection and segmentation of cells in neural images, allowing cellular level connectivity analysis [97]. (f) 3D reconstruction of neuron using neuTube 1.0 shows synaptic connectivity of hippocampal region with mGRASP labeled synapses [110]. ...
... Particularly, deep learning-based approaches have been instrumental in advancing cell detection [96]. CNNs have been trained to detect and segment densely packed cells automatically, even in partially labeled datasets, revealing crucial topographical information and into possible celltype-specific functions about PV cells in the STN [10,97] (Fig. 1e). Another method uses DNN to automatically detect 3D soma in a mouse whole-brain image, which allows the detection of a large population of cells [98]. ...
Mapping neural connections within the brain has been a fundamental goal in neuroscience to understand better its functions and changes that follow aging and diseases. Developments in imaging technology, such as microscopy and labeling tools, have allowed researchers to visualize this connectivity through high-resolution brain-wide imaging. With this, image processing and analysis have become more crucial. However, despite the wealth of neural images generated, access to an integrated image processing and analysis pipeline to process these data is challenging due to scattered information on available tools and methods. To map the neural connections, registration to atlases and feature extraction through segmentation and signal detection are necessary. In this review, our goal is to provide an updated overview of recent advances in these image-processing methods, with a particular focus on fluorescent images of the mouse brain. Our goal is to outline a pathway toward an integrated image-processing pipeline tailored for connecto-informatics. An integrated workflow of these image processing will facilitate researchers’ approach to mapping brain connectivity to better understand complex brain networks and their underlying brain functions. By highlighting the image-processing tools available for fluroscent imaging of the mouse brain, this review will contribute to a deeper grasp of connecto-informatics, paving the way for better comprehension of brain connectivity and its implications.
Nucleus segmentation is an imperative step in the qualitative study of imaging datasets, considered as an intricate task in histopathology image analysis. Segmenting a nucleus is an important part of diagnosing, staging, and grading cancer, but overlapping regions make it hard to separate and tell apart independent nuclei. Deep Learning is swiftly paving its way in the arena of nucleus segmentation, attracting quite a few researchers with its numerous published research articles indicating its efficacy in the field. This paper presents a systematic survey on nucleus segmentation using deep learning in the last five years (2017–2021), highlighting various segmentation models (U-Net, SCPP-Net, Sharp U-Net, and LiverNet) and exploring their similarities, strengths, datasets utilized, and unfolding research areas.
Examination of tissue biopsy and quantification of the various characteristics of cellular processes are clinical benchmarks in cancer diagnosis. Nuclei and glands instance segmentation greatly assists the high-throughput quantification of cellular process and accurate appraisal of tissue biopsy. It subsequently makes a significant improvement to the computational pathology process for cancer diagnosis, treatment planning, and survival analysis. Recent advancements in the field of computer vision have automated the manual, laborious, and time-consuming histopathological analysis process. Automated image analysis of histopathological images for cells and tissues to trace the entirety of the ultrastructures, has been an active area of research in medical informatics for decades. The developments in computers, microscopy hardware, and the availability of large-scale public datasets have further fastened the development in this field. And the realization that scientific and diagnostic pathology calls for fresh ways to undertake, automated image analysis of histopathological images has captivated contemporary attention. In this survey, 126 papers illustrating the AI-based methods for nuclei and glands instance segmentation published in the last five years (2017–2022) are deeply analyzed, and the limitations of current approaches and the open challenges are discussed. Moreover, the potential future research direction is presented, and the contribution of state-of-the-art methods is summarized. Further, a generalized summary of publicly available datasets and detailed insights on the grand challenges illustrating the top-performing methods specific to each challenge is also provided. Besides, we intended to give the reader the current state of existing research and pointers to the future directions in developing methods that can be used in clinical practice enabling improved diagnosis, grading, prognosis, and treatment planning of cancer. To the best of our knowledge, no previous work has reviewed the instance segmentation in histology images focusing on nuclei and glands instance segmentation.
Neoplastic cells are tumorous cells that damage the cells around them and are the prologue for cancer development in organs. However, identifying these cells poses a bottle-neck in the research of cancer cure as it is an extremely tedious job to manually isolate these from the rest of the cells in the tissue. Hence, the automation of this process using deep learning (DL)-based object detection and segmentation techniques such as Mask R-CNN will allow researchers and pathologists to save valuable time otherwise consumed in manually identifying these nuclei. The main objective of this research paper is to provide an instance segmentation technique to label and segment neoplastic cell nuclei from multiple instances of whole-slide images (WSI). For this process, a contemporary neural network architecture called the mask region-based convolutional neural network (Mask R-CNN) was used. This proposed technique generates a pixel-wise binary mask. These masks are capable of segmenting these instances and facilitating the advancement of intelligent systems in medical imaging and computational pathology. This time can instead be devoted to developing better cures by conducting more research. The paper also highlights the best techniques and practices that can be employed while training a model for a task of such complexity. The results of these techniques provide a mean average precision (mAP) score of 0.756 and a binary panoptic quality (bPQ) score of 0.675.KeywordsMedical imagingImage processingNeoplastic cellDeep learningComputer visionSegmentationComputational pathologyMask R-CNNCancer research
The subthalamic nucleus (STN) controls psychomotor activity and is an efficient therapeutic deep brain stimulation target in individuals with Parkinson's disease. Despite evidence indicating position-dependent therapeutic effects and distinct functions within the STN, the input circuit and cellular profile in the STN remain largely unclear. Using neuroanatomical techniques, we construct a comprehensive connectivity map of the indirect and hyperdirect pathways in the mouse STN. Our circuit- and cellular-level connectivities reveal a topographically graded organization with three types of indirect and hyperdirect pathways (external globus pallidus only, STN only, and collateral). We confirm consistent pathways into the human STN by 7 T MRI-based tractography. We identify two functional types of topographically distinct glutamatergic STN neurons (parvalbumin [PV+/−]) with synaptic connectivity from indirect and hyperdirect pathways. Glutamatergic PV+ STN neurons contribute to burst firing. These data suggest a complex interplay of information integration within the basal ganglia underlying coordinated movement control and therapeutic effects.
Early diagnosis of signet ring cell carcinoma dramatically improves the survival rate of patients. Due to lack of public dataset and expert-level annotations, automatic detection on signet ring cell (SRC) has not been thoroughly investigated. In MICCAI DigestPath2019 challenge, apart from foreground (SRC region)-background (normal tissue area) class imbalance, SRCs are partially annotated due to costly medical image annotation, which introduces extra label noise. To address the issues simultaneously, we propose Decoupled Gradient Harmonizing Mechanism (DGHM) and embed it into classification loss, denoted as DGHM-C loss. Specifically, besides positive (SRCs) and negative (normal tissues) examples, we further decouple noisy examples from clean examples and harmonize the corresponding gradient distributions in classification respectively. Without whistles and bells, we achieved the 2nd place in the challenge. Ablation studies and controlled label missing rate experiments demonstrate that DGHM-C loss can bring substantial improvement in partially annotated object detection.
