Automatic Nuclei Segmentation Research Articles

Enhanced Nuclei Segmentation and Classification via Category Descriptors in the SAM Model.

Segmenting and classifying nuclei in H&E histopathology images is often limited by the long-tailed distribution of nuclei types. However, the strong generalization ability of image segmentation foundation models like the Segment Anything Model (SAM) can help improve the detection quality of rare types of nuclei. In this work, we introduce category descriptors to perform nuclei segmentation and classification by prompting the SAM model. We close the domain gap between histopathology and natural scene images by aligning features in low-level space while preserving the high-level representations of SAM. We performed extensive experiments on the Lizard dataset, validating the ability of our model to perform automatic nuclei segmentation and classification, especially for rare nuclei types, where achieved a significant detection improvement in the F1 score of up to 12%. Our model also maintains compatibility with manual point prompts for interactive refinement during inference without requiring any additional training.

Real-time and accurate deep learning-based multi-organ nucleus segmentation in histology images

Automated nucleus segmentation is considered the gold standard for diagnosing some severe diseases. Accurate instance segmentation of nuclei is still very challenging because of the large number of clustered nuclei, and the different appearance of nuclei for different tissue types. In this paper, a neural network is proposed for fast and accurate instance segmentation of nuclei in histopathology images. The network is inspired by the Unet and residual nets. The main contribution of the proposed model is enhancing the classification accuracy of nuclear boundaries by moderately preserving the spatial features by relatively d the size of feature maps. Then, a proposed 2D convolution layer is used instead of the conventional 3D convolution layer, the core of CNN-based architectures, where the feature maps are first compacted before being convolved by 2D kernel filters. This significantly reduces the processing time and avoids the out of memory problem of the GPU. Also, more features are extracted when getting deeper into the network without degrading the spatial features dramatically. Hence, the number of layers, required to compensate the loss of spatial features, is reduced that also reduces the processing time. The proposed approach is applied to two multi-organ datasets and evaluated by the Aggregated Jaccard Index (AJI), F1-score and the number of frames per second. Also, the formula of AJI is modified to reflect the object- and pixel-level errors more accurately. The proposed model is compared to some state-of-the-art architectures, and it shows better performance in terms of the segmentation speed and accuracy.

Nuclei segmentation using attention aware and adversarial networks

Accurate segmentation of nuclei plays a critical role in pathology since assessments and diagnoses are mainly based on the recognition, measurement, and counting of nuclei. However, in digital pathology, automated nucleus segmentation is a challenging issue because of various factors such as color inconsistency in stained images, unclear nucleus boundaries, low contrast between background and nuclei, different shapes and sizes of nuclei, and intensity inhomogeneity not only inside nuclei but also across them and the background. In this work, an efficient method has been developed for nuclei segmentation. Its efficiency has been achieved through tissue and chemical invariant normalization, feature extraction with dense convolution layers, merging of local and global features, mask generation, and adversarial learning. For fair comparisons, state-of-the-art nuclei segmentations have been employed to the same data sets, and their performances have been evaluated using the same metrics. This paper’s main contributions are threefold: (i) Introducing a novel technique for nuclei segmentation using an adversarial network and a hybrid attention-aware network. (ii) Presenting the effective merging of global and local features to enhance pattern recognition, and the utilization of efficient hybrid attention blocks for extracting desired global information and improving relationships between feature regions at different locations. (iii) Presenting experimental results showing that the proposed technique accomplishes nuclei detection and extraction with higher accuracy (a minimum improvement of 3.7%) than other recent methods. Also, each stage provides considerable contributions to the segmentation performance. Particularly, the hybrid attention-aware network has improved the performance by 4.2% according to the dice coefficient.

PointNu-Net: Keypoint-Assisted Convolutional Neural Network for Simultaneous Multi-Tissue Histology Nuclei Segmentation and Classification

Automatic nuclei segmentation and classification play a vital role in digital pathology. However, previous works are mostly built on data with limited diversity and small sizes, making the results questionable or misleading in actual downstream tasks. In this article, we aim to build a reliable and robust method capable of dealing with data from the ‘the clinical wild’. Specifically, we study and design a new method to simultaneously detect, segment, and classify nuclei from Haematoxylin and Eosin (H&E) stained histopathology data, and evaluate our approach using the recent largest dataset: PanNuke. We address the detection and classification of each nuclei as a novel semantic keypoint estimation problem to determine the center point of each nuclei. Next, the corresponding class-agnostic masks for nuclei center points are obtained using dynamic instance segmentation. Meanwhile, we proposed a novel Joint Pyramid Fusion Module (JPFM) to model the cross-scale dependencies, thus enhancing the local feature for better nuclei detection and classification. By decoupling two simultaneous challenging tasks and taking advantage of JPFM, our method can benefit from class-aware detection and class-agnostic segmentation, thus leading to a significant performance boost. We demonstrate the superior performance of our proposed approach for nuclei segmentation and classification across 19 different tissue types, delivering new benchmark results.

An Automated Framework for Histopathological Nucleus Segmentation with Deep Attention Integrated Networks.

Clinical management and accurate disease diagnosis are evolving from qualitative stage to the quantitative stage, particularly at the cellular level. However, the manual process of histopathological analysis is lab-intensive and time-consuming. Meanwhile, the accuracy is limited by the experience of the pathologist. Therefore, deep learning-empowered computer-aided diagnosis (CAD) is emerging as an important topic in digital pathology to streamline the standard process of automatic tissue analysis. Automated accurate nucleus segmentation can not only help pathologists make more accurate diagnosis, save time and labor, but also achieve consistent and efficient diagnosis results. However, nucleus segmentation is susceptible to staining variation, uneven nucleus intensity, background noises, and nucleus tissue differences in biopsy specimens. To solve these problems, we propose Deep Attention Integrated Networks (DAINets), which mainly built on self-attention based spatial attention module and channel attention module. In addition, we also introduce a feature fusion branch to fuse high-level representations with low-level features for multi-scale perception, and employ the mark-based watershed algorithm to refine the predicted segmentation maps. Furthermore, in the testing phase, we design Individual Color Normalization (ICN) to settle the dyeing variation problem in specimens. Quantitative evaluations on the multi-organ nucleus dataset indicate the priority of our automated nucleus segmentation framework.

Semi-supervised nuclei segmentation based on multi-edge features fusion attention network.

The morphology of the nuclei represents most of the clinical pathological information, and nuclei segmentation is a vital step in current automated histopathological image analysis. Supervised machine learning-based segmentation models have already achieved outstanding performance with sufficiently precise human annotations. Nevertheless, outlining such labels on numerous nuclei is extremely professional needing and time consuming. Automatic nuclei segmentation with minimal manual interventions is highly needed to promote the effectiveness of clinical pathological researches. Semi-supervised learning greatly reduces the dependence on labeled samples while ensuring sufficient accuracy. In this paper, we propose a Multi-Edge Feature Fusion Attention Network (MEFFA-Net) with three feature inputs including image, pseudo-mask and edge, which enhances its learning ability by considering multiple features. Only a few labeled nuclei boundaries are used to train annotations on the remaining mostly unlabeled data. The MEFFA-Net creates more precise boundary masks for nucleus segmentation based on pseudo-masks, which greatly reduces the dependence on manual labeling. The MEFFA-Block focuses on the nuclei outline and selects features conducive to segment, making full use of the multiple features in segmentation. Experimental results on public multi-organ databases including MoNuSeg, CPM-17 and CoNSeP show that the proposed model has the mean IoU segmentation evaluations of 0.706, 0.751, and 0.722, respectively. The model also achieves better results than some cutting-edge methods while the labeling work is reduced to 1/8 of common supervised strategies. Our method provides a more efficient and accurate basis for nuclei segmentations and further quantifications in pathological researches.

Abstract 5409: NuKit: a deep learning platform for histopathological Images

Abstract Hematoxylin and eosin (H&E) staining is the most common type of histopathological images used for quantitative data analysis. For histopathological image analysis, nucleus segmentation represents one of the initial steps in quantitative data analysis pipelines. Recently, deep learning methods for nucleus segmentation on histopathological images become the mainstream in digital pathology. However, adopting these pre-trained models in digital pathology or clinical research remains limited. Two main factors limit the usage of deep learning methods in routine research: 1) many models required some technical background to execute the programs, and 2) the speed of returning the results to the users. To overcome these limitations, we have developed and implemented NuKit, a deep learning platform which accelerates nucleus segmentation and provides prompt results to the users. NuKit platform consists of two deep learning models coupled with an interactive graphical user interface (GUI) to provide fast and automatic nucleus segmentation “on the fly”. The two deep learning models are: 1) the whole image segmentation model and 2) the click segmentation model. Both deep learning models provide complementary tasks in nucleus segmentation in the NuKit platform. The whole image segmentation model performs whole image nucleus whereas the click segmentation model supplements the nucleus segmentation with user-driven input to edits the segmented nuclei. We used PanNuke as the training data set which contains 160,368 nuclei extracted from 7,901 image tiles. The sources of this data set were curated from The Cancer Genome Atlas (TCGA) and included a very few in-house data. For test sets, we used several popular data sets such as TCGA (not used in the training set). We used DICE coefficient as the metric for comparisons of NuKit with other deep learning methods. For the results, we observed that NuKit achieved comparable results with the state-of-the-art deep learning methods and outperforms those non-deep learning methods. For TNBC and CoNSeP data sets, NuKit achieved DICE 0.793 and 0.854 comparing to HoVer-Net 0.749 and 0.664. For CoNSeP test data set, NuKit achieved DICE 0.860 comparing to Cell Profiler 0.434 and QuPath 0.588. In addition, both pre-trained models were embedded in the NuKit GUI, which provides interactive and prompt response to users in analyzing their histopathological images. The outputs of NuKit are interoperable with other quantitative image analysis tools to facilitate the ecosystem of computational pathology to many formats to combine with other software to construct pipelines. In summary, we have introduced NuKit, an innovation platform which combines two deep learning models coupled with interactive GUI to accelerate nucleus segmentation task in histopathological images. We believe that NuKit provides a new platform to bridge the pre-trained deep learning models into digital pathology and clinical usage. Citation Format: Ching-Nung Lin, Christine H. Chung, Aik Choon Tan. NuKit: a deep learning platform for histopathological Images. [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 5409.

Automatic Segmentation of Parkinson Disease Therapeutic Targets Using Nonlinear Registration and Clinical MR Imaging: Comparison of Methodology, Presence of Disease, and Quality Control

Introduction: Accurate and precise delineation of the globus pallidus pars interna (GPi) and subthalamic nucleus (STN) is critical for the clinical treatment and research of Parkinson’s disease (PD). Automated segmentation is a developing technology which addresses limitations of visualizing deep nuclei on MR imaging and standardizing their definition in research applications. We sought to compare manual segmentation with three workflows for template-to-patient nonlinear registration providing atlas-based automatic segmentation of deep nuclei. Methods: Bilateral GPi, STN, and red nucleus (RN) were segmented for 20 PD and 20 healthy control (HC) subjects using 3T MRIs acquired for clinical purposes. The automated workflows used were an option available in clinical practice and two common research protocols. Quality control (QC) was performed on registered templates via visual inspection of readily discernible brain structures. Manual segmentation using T1, proton density, and T2 sequences was used as “ground truth” data for comparison. Dice similarity coefficient (DSC) was used to assess agreement between segmented nuclei. Further analysis was done to compare the influences of disease state and QC classifications on DSC. Results: Automated segmentation workflows (CIT-S, CRV-AB, and DIST-S) had the highest DSC for the RN and lowest for the STN. Manual segmentations outperformed automated segmentation for all workflows and nuclei; however, for 3/9 workflows (CIT-S STN, CRV-AB STN, and CRV-AB GPi) the differences were not statically significant. HC and PD only showed significant differences in 1/9 comparisons (DIST-S GPi). QC classification only demonstrated significantly higher DSC in 2/9 comparisons (CRV-AB RN and GPi). Conclusion: Manual segmentations generally performed better than automated segmentations. Disease state does not appear to have a significant effect on the quality of automated segmentations via nonlinear template-to-patient registration. Notably, visual inspection of template registration is a poor indicator of the accuracy of deep nuclei segmentation. As automatic segmentation methods continue to evolve, efficient and reliable QC methods will be necessary to support safe and effective integration into clinical workflows.

An imbalance-aware nuclei segmentation methodology for H&E stained histopathology images

A key step in computational pathology is to automate the laborious process of manual nuclei segmentation in Hematoxylin and Eosin (H&E) stained whole slide images (WSIs). Despite lots of efforts put forward by the researchers to develop automated nuclei segmentation methodologies in the literature, the segmentation performance is still constrained due to several challenges, including overlapping and clumped nuclei, scanners with different resolutions and nuclei with varying sizes and shapes. In this paper, we introduce an imbalance-aware nuclei segmentation methodology to deal with class imbalance problems in H&E stained histopathology images. The introduced methodology involves the following improvements: (1) the design of a preprocessing stage with a variety of resize-split, augmentation and normalization techniques, and (2) an enhanced lightweight U-Net architecture with a generalized Dice loss layer. To prove its effectiveness and efficiency, a comprehensive experimental study is carried out on a well-known benchmark, namely the MonuSeg2018 dataset. According to the results, the proposed methodology outperforms various recently introduced studies in terms of well-known evaluation metrics, such as Aggregated Jaccard Index (AJI) and Intersection of Union (IoU).

NuKit: A deep learning platform for fast nucleus segmentation of histopathological images.

Nucleus segmentation represents the initial step for histopathological image analysis pipelines, and it remains a challenge in many quantitative analysis methods in terms of accuracy and speed. Recently, deep learning nucleus segmentation methods have demonstrated to outperform previous intensity- or pattern-based methods. However, the heavy computation of deep learning provides impression of lagging response in real time and hampered the adoptability of these models in routine research. We developed and implemented NuKit a deep learning platform, which accelerates nucleus segmentation and provides prompt results to the users. NuKit platform consists of two deep learning models coupled with an interactive graphical user interface (GUI) to provide fast and automatic nucleus segmentation "on the fly". Both deep learning models provide complementary tasks in nucleus segmentation. The whole image segmentation model performs whole image nucleus whereas the click segmentation model supplements the nucleus segmentation with user-driven input to edits the segmented nuclei. We trained the NuKit whole image segmentation model on a large public training data set and tested its performance in seven independent public image data sets. The whole image segmentation model achieves average [Formula: see text] and [Formula: see text]. The outputs could be exported into different file formats, as well as provides seamless integration with other image analysis tools such as QuPath. NuKit can be executed on Windows, Mac, and Linux using personal computers.

An effective approach for the nuclei segmentation from breast histopathological images using star-convex polygon

Early detection and patient-driven precise treatment can drastically reduce the elevation in the mortality rate due to breast tumours in women. Therefore, it is inevitable to develop an interpretable model, which detects and segments cancer cell nuclei. The main challenge to achieve this goal is the separation of the individual cell nucleus from the clustered overlapping nuclei without losing the pixel-level shape correspondence of each nucleus. The performance of the most common image processing conventional methods alone has not yielded the expected result for nuclei segmentation from the digitized tissue images either due to over-segmentation or under-segmentation. Here, this issue is addressed by developing an automatic nuclei segmentation algorithm which uses a deep learning-based approach. This paper introduces pixel-level analysis using the star convex polygon approach for segmenting the cell nuclei from the histopathological images. Analysis shows that this technique exemplifies a more accurate segmentation result with a validation IOU score of 87.56%. The true positive rate and the detection of pixel-level shape correspondence are also comparatively high for this method during the test phase.

Stiffness-Controlled Hydrogels for 3D Cell Culture Models.

Nanofibrillated cellulose (NFC) hydrogel is a versatile biomaterial suitable, for example, for three-dimensional (3D) cell spheroid culturing, drug delivery, and wound treatment. By freeze-drying NFC hydrogel, highly porous NFC structures can be manufactured. We freeze-dried NFC hydrogel and subsequently reconstituted the samples into a variety of concentrations of NFC fibers, which resulted in different stiffness of the material, i.e., different mechanical cues. After the successful freeze-drying and reconstitution, we showed that freeze-dried NFC hydrogel can be used for one-step 3D cell spheroid culturing of primary mesenchymal stem/stromal cells, prostate cancer cells (PC3), and hepatocellular carcinoma cells (HepG2). No difference was observed in the viability or morphology between the 3D cell spheroids cultured in the freeze-dried and reconstituted NFC hydrogel and fresh NFC hydrogel. Furthermore, the 3D cultured spheroids showed stable metabolic activity and nearly 100% viability. Finally, we applied a convolutional neural network (CNN)-based automatic nuclei segmentation approach to automatically segment individual cells of 3D cultured PC3 and HepG2 spheroids. These results provide an application to culture 3D cell spheroids more readily with the NFC hydrogel and a step towards automatization of 3D cell culturing and analysis.

Nuclei segmentation of HE stained histopathological images based on feature global delivery connection network.

The analysis of pathological images, such as cell counting and nuclear morphological measurement, is an essential part in clinical histopathology researches. Due to the diversity of uncertain cell boundaries after staining, automated nuclei segmentation of Hematoxylin-Eosin (HE) stained pathological images remains challenging. Although better performances could be achieved than most of classic image processing methods do, manual labeling is still necessary in a majority of current machine learning based segmentation strategies, which restricts further improvements of efficiency and accuracy. Aiming at the requirements of stable and efficient high-throughput pathological image analysis, an automated Feature Global Delivery Connection Network (FGDC-net) is proposed for nuclei segmentation of HE stained images. Firstly, training sample patches and their corresponding asymmetric labels are automatically generated based on a Full Mixup strategy from RGB to HSV color space. Secondly, in order to add connections between adjacent layers and achieve the purpose of feature selection, FGDC module is designed by removing the jumping connections between codecs commonly used in UNet-based image segmentation networks, which learns the relationships between channels in each layer and pass information selectively. Finally, a dynamic training strategy based on mixed loss is used to increase the generalization capability of the model by flexible epochs. The proposed improvements were verified by the ablation experiments on multiple open databases and own clinical meningioma dataset. Experimental results on multiple datasets showed that FGDC-net could effectively improve the segmentation performances of HE stained pathological images without manual interventions, and provide valuable references for clinical pathological analysis.

Meta multi-task nuclei segmentation with fewer training samples.

Cells/nuclei deliver massive information of microenvironment. An automatic nuclei segmentation approach can reduce pathologists' workload and allow precise of the microenvironment for biological and clinical researches. Existing deep learning models have achieved outstanding performance under the supervision of a large amount of labeled data. However, when data from the unseen domain comes, we still have to prepare a certain degree of manual annotations for training for each domain. Unfortunately, obtaining histopathological annotations is extremely difficult. It is high expertise-dependent and time-consuming. In this paper, we attempt to build a generalized nuclei segmentation model with less data dependency and more generalizability. To this end, we propose a meta multi-task learning (Meta-MTL) model for nuclei segmentation which requires fewer training samples. A model-agnostic meta-learning is applied as the outer optimization algorithm for the segmentation model. We introduce a contour-aware multi-task learning model as the inner model. A feature fusion and interaction block (FFIB) is proposed to allow feature communication across both tasks. Extensive experiments prove that our proposed Meta-MTL model can improve the model generalization and obtain a comparable performance with state-of-the-art models with fewer training samples. Our model can also perform fast adaptation on the unseen domain with only a few manual annotations. Code is available at https://github.com/ChuHan89/Meta-MTL4NucleiSegmentation.

Marker-controlled watershed with deep edge emphasis and optimized H-minima transform for automatic segmentation of densely cultivated 3D cell nuclei

BackgroundThe segmentation of 3D cell nuclei is essential in many tasks, such as targeted molecular radiotherapies (MRT) for metastatic tumours, toxicity screening, and the observation of proliferating cells. In recent years, one popular method for automatic segmentation of nuclei has been deep learning enhanced marker-controlled watershed transform. In this method, convolutional neural networks (CNNs) have been used to create nuclei masks and markers, and the watershed algorithm for the instance segmentation. We studied whether this method could be improved for the segmentation of densely cultivated 3D nuclei via developing multiple system configurations in which we studied the effect of edge emphasizing CNNs, and optimized H-minima transform for mask and marker generation, respectively.ResultsThe dataset used for training and evaluation consisted of twelve in vitro cultivated densely packed 3D human carcinoma cell spheroids imaged using a confocal microscope. With this dataset, the evaluation was performed using a cross-validation scheme. In addition, four independent datasets were used for evaluation. The datasets were resampled near isotropic for our experiments. The baseline deep learning enhanced marker-controlled watershed obtained an average of 0.69 Panoptic Quality (PQ) and 0.66 Aggregated Jaccard Index (AJI) over the twelve spheroids. Using a system configuration, which was otherwise the same but used 3D-based edge emphasizing CNNs and optimized H-minima transform, the scores increased to 0.76 and 0.77, respectively. When using the independent datasets for evaluation, the best performing system configuration was shown to outperform or equal the baseline and a set of well-known cell segmentation approaches.ConclusionsThe use of edge emphasizing U-Nets and optimized H-minima transform can improve the marker-controlled watershed transform for segmentation of densely cultivated 3D cell nuclei. A novel dataset of twelve spheroids was introduced to the public.

SONNET: A Self-Guided Ordinal Regression Neural Network for Segmentation and Classification of Nuclei in Large-Scale Multi-Tissue Histology Images.

Automated nuclei segmentation and classification are the keys to analyze and understand the cellular characteristics and functionality, supporting computer-aided digital pathology in disease diagnosis. However, the task still remains challenging due to the intrinsic variations in size, intensity, and morphology of different types of nuclei. Herein, we propose a self-guided ordinal regression neural network for simultaneous nuclear segmentation and classification that can exploit the intrinsic characteristics of nuclei and focus on highly uncertain areas during training. The proposed network formulates nuclei segmentation as an ordinal regression learning by introducing a distance decreasing discretization strategy, which stratifies nuclei in a way that inner regions forming a regular shape of nuclei are separated from outer regions forming an irregular shape. It also adopts a self-guided training strategy to adaptively adjust the weights associated with nuclear pixels, depending on the difficulty of the pixels that is assessed by the network itself. To evaluate the performance of the proposed network, we employ large-scale multi-tissue datasets with 276349 exhaustively annotated nuclei. We show that the proposed network achieves the state-of-the-art performance in both nuclei segmentation and classification in comparison to several methods that are recently developed for segmentation and/or classification.

MSAL-Net: improve accurate segmentation of nuclei in histopathology images by multiscale attention learning network

BackgroundThe digital pathology images obtain the essential information about the patient’s disease, and the automated nuclei segmentation results can help doctors make better decisions about diagnosing the disease. With the speedy advancement of convolutional neural networks in image processing, deep learning has been shown to play a significant role in the various analysis of medical images, such as nuclei segmentation, mitosis detection and segmentation etc. Recently, several U-net based methods have been developed to solve the automated nuclei segmentation problems. However, these methods fail to deal with the weak features representation from the initial layers and introduce the noise into the decoder path. In this paper, we propose a multiscale attention learning network (MSAL-Net), where the dense dilated convolutions block captures more comprehensive nuclei context information, and a newly modified decoder part is introduced, which integrates with efficient channel attention and boundary refinement modules to effectively learn spatial information for better prediction and further refine the nuclei cell of boundaries.ResultsBoth qualitative and quantitative results are obtained on the publicly available MoNuseg dataset. Extensive experiment results verify that our proposed method significantly outperforms state-of-the-art methods as well as the vanilla Unet method in the segmentation task. Furthermore, we visually demonstrate the effect of our modified decoder part.ConclusionThe MSAL-Net shows superiority with a novel decoder to segment the touching and blurred background nuclei cells obtained from histopathology images with better performance for accurate decoding.

Automatic segmentation of deep grey nuclei using a high-resolution 7T magnetic resonance imaging atlas-Quantification of T1 values in healthy volunteers.

We present a new consensus atlas of deep grey nuclei obtained by shape-based averaging of manual segmentation of two experienced neuroradiologists and optimized from 7T MP2RAGE images acquired at (.6mm)3 in 60 healthy subjects. A group-wise normalization method was used to build a high-contrast and high-resolution T1 -weighted brain template (.5mm)3 using data from 30 out of the 60 controls. Delineation of 24 deep grey nuclei per hemisphere, including the claustrum and 12 thalamic nuclei, was then performed by two expert neuroradiologists and reviewed by a third neuroradiologist according to tissue contrast and external references based on the Morel atlas. Corresponding deep grey matter structures were also extracted from the Morel and CIT168 atlases. The data-derived, Morel and CIT168 atlases were all applied at the individual level using non-linear registration to fit the subject reference and to extract absolute mean quantitative T1 values derived from the 3D-MP2RAGE volumes, after correction for residual B1+ biases. Three metrics (the Dice and the volumetric similarity coefficients and a novel Hausdorff distance) were used to estimate the inter-rater agreement of manual MRI segmentation and inter-atlas variability, and these metrics were measured to quantify biases due to image registration, and their impact on the measurements of the quantitative T1 values was highlighted. This represents a fully automated segmentation process permitting the extraction of unbiased normative T1 values in a population of young healthy controls as a reference for characterizing subtle structural alterations of deep grey nuclei relevant to a range of neurological diseases.

Nuclei Segmentation on Histopathology Images of Breast Carcinoma.

With the use of computer-aided diagnostic systems, the automatic detection and segmentation of the cell nuclei have become essential in pathology due to cellular nuclei counting and nuclear pleomorphism analysis are critical for the classification and grading of breast cancer histopathology. This work describes a methodology for automatic detection and segmentation of cellular nuclei in breast cancer histopathology images obtained from the BreakHis database, the Standford tissue microarray database, and the Breast Cancer Cell Segmentation database. The proposed scheme is based on the characterization of Hematoxylin and Eosin (H&E) staining, size, and shape features. In addition, we use the information obtained from morphological transformations and adaptive intensity adjustments to detect and separate each cell nucleus detected in the image. The segmentation was carried out by testing the proposed methodology in a histological breast cancer database that provides the associated groundtruth segmentation. Subsequently, the Sørensen-Dice similarity coefficient was calculated to analyze the suitability of the results.Clinical relevance- In this work, the detection and segmentation of cell nuclei in breast cancer histological images are carried out automatically. The method can identify cell nuclei regardless of variations in the level of staining and image magnification. Moreover, a granulometric analysis of the components allows identifying cell clumps and segment them into individual cell nuclei. Improved identification of cell nuclei under different image conditions was demonstrated to reach a sensitivity average of 0.76 ± 0.12. The results provide a base for further and complex processes such as cell counting, feature analysis, and nuclear pleomorphism, which are relevant tasks in the evaluation and diagnostic performed by the expert pathologist.

Blur-Robust Nuclei Segmentation for Immunofluorescence Images.

Automated nuclei segmentation from immunofluorescence (IF) microscopic image is a crucial first step in digital pathology. A lot of research has been devoted to develop novel nuclei segmentation algorithms to give high performance on good quality images. However, fewer methods were developed for poor-quality images like out-of-focus (blurry) data. In this work, we take a principled approach to study the performance of nuclei segmentation algorithms on out-of-focus images for different levels of blur. A deep learning encoder-decoder framework with a novel Y forked decoder is proposed here. The two fork ends are tied to segmentation and deblur output. The addition of a separate deblurring task in the training paradigm helps to regularize the network on blurry images. Our proposed method accurately predicts the instance nuclei segmentation on sharp as well as out-of-focus images. Additionally, predicted deblurred image provides interpretable insights to experts. Experimental analysis on the Human U2OS cells (out-of-focus) dataset shows that our algorithm is robust and outperforms the state-of-the-art methods.