A three in one bottom-up framework for simultaneous semantic segmentation, instance segmentation and classification of multi-organ nuclei in digital cancer histology

• URL: http://arxiv.org/abs/2308.11179v1
• Date: Tue, 22 Aug 2023 04:10:14 GMT
• Title: A three in one bottom-up framework for simultaneous semantic segmentation, instance segmentation and classification of multi-organ nuclei in digital cancer histology
• Authors: Ibtihaj Ahmad, Syed Muhammad Israr, Zain Ul Islam
• Abstract summary: Simultaneous segmentation and classification of nuclei in digital histology play an essential role in computer-assisted cancer diagnosis. The highest achieved binary and multi-class Panoptic Quality (PQ) remains as low as 0.68 bPQ and 0.49 mPQ, respectively. This work extends our previous model to simultaneous instance segmentation and classification.
• Score: 3.2228025627337864
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Simultaneous segmentation and classification of nuclei in digital histology play an essential role in computer-assisted cancer diagnosis; however, it remains challenging. The highest achieved binary and multi-class Panoptic Quality (PQ) remains as low as 0.68 bPQ and 0.49 mPQ, respectively. It is due to the higher staining variability, variability across the tissue, rough clinical conditions, overlapping nuclei, and nuclear class imbalance. The generic deep-learning methods usually rely on end-to-end models, which fail to address these problems associated explicitly with digital histology. In our previous work, DAN-NucNet, we resolved these issues for semantic segmentation with an end-to-end model. This work extends our previous model to simultaneous instance segmentation and classification. We introduce additional decoder heads with independent weighted losses, which produce semantic segmentation, edge proposals, and classification maps. We use the outputs from the three-head model to apply post-processing to produce the final segmentation and classification. Our multi-stage approach utilizes edge proposals and semantic segmentations compared to direct segmentation and classification strategies followed by most state-of-the-art methods. Due to this, we demonstrate a significant performance improvement in producing high-quality instance segmentation and nuclei classification. We have achieved a 0.841 Dice score for semantic segmentation, 0.713 bPQ scores for instance segmentation, and 0.633 mPQ for nuclei classification. Our proposed framework is generalized across 19 types of tissues. Furthermore, the framework is less complex compared to the state-of-the-art.

Related papers

• Topologically Faithful Multi-class Segmentation in Medical Images [43.6770098513581]
  We propose a general loss function for topologically faithful multi-class segmentation. We project the N-class segmentation problem to N single-class segmentation tasks. Our loss formulation significantly enhances topological correctness in cardiac, cell, artery-vein, and Circle of Willis segmentation.
  arXiv  Detail & Related papers  (2024-03-16T19:11:57Z)
• Prompt-based Grouping Transformer for Nucleus Detection and Classification [70.55961378096116]
  nuclei detection and classification can produce effective information for disease diagnosis. Most existing methods classify nuclei independently or do not make full use of the semantic similarity between nuclei and their grouping features. We propose a novel end-to-end nuclei detection and classification framework based on a grouping transformer-based classifier.
  arXiv  Detail & Related papers  (2023-10-22T04:50:48Z)
• Rethinking Semi-Supervised Medical Image Segmentation: A Variance-Reduction Perspective [51.70661197256033]
  We propose ARCO, a semi-supervised contrastive learning framework with stratified group theory for medical image segmentation. We first propose building ARCO through the concept of variance-reduced estimation and show that certain variance-reduction techniques are particularly beneficial in pixel/voxel-level segmentation tasks. We experimentally validate our approaches on eight benchmarks, i.e., five 2D/3D medical and three semantic segmentation datasets, with different label settings.
  arXiv  Detail & Related papers  (2023-02-03T13:50:25Z)
• MGTUNet: An new UNet for colon nuclei instance segmentation and quantification [3.693770437824002]
  This paper proposes a new UNet model for handling nuclei based on the UNet framework, called MGTUNet. By comparing the three evaluation metrics and the parameter sizes of the models, MGTUNet obtained 0.6254 on PQ, 0.6359 on mPQ, and 0.8695 on R2.
  arXiv  Detail & Related papers  (2022-10-20T03:00:40Z)
• Comparative analysis of deep learning approaches for AgNOR-stained cytology samples interpretation [52.77024349608834]
  This paper provides a way to analyze argyrophilic nucleolar organizer regions (AgNOR) stained slide using deep learning approaches. Our results show that the semantic segmentation using U-Net with ResNet-18 or ResNet-34 as the backbone have similar results. The best model shows an IoU for nucleus, cluster, and satellites of 0.83, 0.92, and 0.99 respectively.
  arXiv  Detail & Related papers  (2022-10-19T15:15:32Z)
• Domain Adaptive Nuclei Instance Segmentation and Classification via Category-aware Feature Alignment and Pseudo-labelling [65.40672505658213]
  We propose a novel deep neural network, namely Category-Aware feature alignment and Pseudo-Labelling Network (CAPL-Net) for UDA nuclei instance segmentation and classification. Our approach outperforms state-of-the-art UDA methods with a remarkable margin.
  arXiv  Detail & Related papers  (2022-07-04T07:05:06Z)
• PointNu-Net: Keypoint-assisted Convolutional Neural Network for Simultaneous Multi-tissue Histology Nuclei Segmentation and Classification [23.466331358975044]
  We study and design a new method to simultaneously detect, segment, and classify nuclei from Haematoxylin and Eosin stained histopathology data. We demonstrate the superior performance of our proposed approach for nuclei segmentation and classification across 19 different tissue types.
  arXiv  Detail & Related papers  (2021-11-01T08:29:40Z)
• Inconsistency-aware Uncertainty Estimation for Semi-supervised Medical Image Segmentation [92.9634065964963]
  We present a new semi-supervised segmentation model, namely, conservative-radical network (CoraNet) based on our uncertainty estimation and separate self-training strategy. Compared with the current state of the art, our CoraNet has demonstrated superior performance.
  arXiv  Detail & Related papers  (2021-10-17T08:49:33Z)
• Bend-Net: Bending Loss Regularized Multitask Learning Network for Nuclei Segmentation in Histopathology Images [65.47507533905188]
  We propose a novel multitask learning network with a bending loss regularizer to separate overlapped nuclei accurately. The newly proposed multitask learning architecture enhances the generalization by learning shared representation from three tasks. The proposed bending loss defines high penalties to concave contour points with large curvatures, and applies small penalties to convex contour points with small curvatures.
  arXiv  Detail & Related papers  (2021-09-30T17:29:44Z)
• Improved AI-based segmentation of apical and basal slices from clinical cine CMR [1.7647111545685723]
  We aim to investigate the performance of AI algorithms in segmenting basal and apical slices. We trained all our models on a large dataset of clinical CMR studies obtained from two NHS hospitals. We show that the classification and segmentation approach was best at reducing the performance gap across all datasets.
  arXiv  Detail & Related papers  (2021-09-20T10:48:50Z)
• Weakly Supervised 3D Classification of Chest CT using Aggregated Multi-Resolution Deep Segmentation Features [5.938730586521215]
  Weakly supervised disease classification of CT imaging suffers from poor localization owing to case-level annotations. We propose a medical classifier that leverages semantic structural concepts learned via multi-resolution segmentation feature maps.
  arXiv  Detail & Related papers  (2020-10-31T00:16:53Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.