Digital pathology and microscopy image analysis are widely employed in the
segmentation of digitally scanned IHC slides, primarily to identify cancer and
pinpoint regions of interest (ROI) indicative of tumor presence. However,
current ROI segmentation models are either stain-specific or suffer from the
issues of stain and scanner variance due to different staining protocols or
modalities across multiple labs. Also, tissues like Ductal Carcinoma in Situ
(DCIS), acini, etc. are often classified as Tumors due to their structural
similarities and color compositions. In this paper, we proposed a novel
convolutional neural network (CNN) based Multi-class Tissue Segmentation model
for histopathology whole-slide Breast slides which classify tumors and segments
other tissue regions such as Ducts, acini, DCIS, Squamous epithelium, Blood
Vessels, Necrosis, etc. as a separate class. Our unique pixel-aligned
non-linear merge across spatial resolutions empowers models with both local and
global fields of view for accurate detection of various classes. Our proposed
model is also able to separate bad regions such as folds, artifacts, blurry
regions, bubbles, etc. from tissue regions using multi-level context from
different resolutions of WSI. Multi-phase iterative training with context-aware
augmentation and increasing noise was used to efficiently train a multi-stain
generic model with partial and noisy annotations from 513 slides. Our training
pipeline used 12 million patches generated using context-aware augmentations
which made our model stain and scanner invariant across data sources. To
extrapolate stain and scanner invariance, our model was evaluated on 23000
patches which were for a completely new stain (Hematoxylin and Eosin) from a
completely new scanner (Motic) from a different lab. The mean IOU was 0.72
which is on par with model performance on other data sources and scanners.

我们提出了一种基于卷积神经网络的多类组织分割模型，通过像素对齐的非线性融合实现了局部和全局视野，能够准确检测各种组织类别，并且对于多种染料和扫描仪具有稳定性。

多染色多层卷积网络用于多组织乳腺癌图像分割

Multi-Stain Multi-Level Convolutional Network for Multi-Tissue Breast  Cancer Image Segmentation

One of the fundamental challenges in microscopy (MS) image analysis is
instance segmentation (IS), particularly when segmenting cluster regions where
multiple objects of varying sizes and shapes may be connected or even
overlapped in arbitrary orientations. Existing IS methods usually fail in
handling such scenarios, as they rely on coarse instance representations such
as keypoints and horizontal bounding boxes (h-bboxes). In this paper, we
propose a novel one-stage framework named A2B-IS to address this challenge and
enhance the accuracy of IS in MS images. Our approach represents each instance
with a pixel-level mask map and a rotated bounding box (r-bbox). Unlike
two-stage methods that use box proposals for segmentations, our method
decouples mask and box predictions, enabling simultaneous processing to
streamline the model pipeline. Additionally, we introduce a Gaussian skeleton
map to aid the IS task in two key ways: (1) It guides anchor placement,
reducing computational costs while improving the model's capacity to learn
RoI-aware features by filtering out noise from background regions. (2) It
ensures accurate isolation of densely packed instances by rectifying erroneous
box predictions near instance boundaries. To further enhance the performance,
we integrate two modules into the framework: (1) An Atrous Attention Block
(A2B) designed to extract high-resolution feature maps with fine-grained
multiscale information, and (2) A Semi-Supervised Learning (SSL) strategy that
leverages both labeled and unlabeled images for model training. Our method has
been thoroughly validated on two large-scale MS datasets, demonstrating its
superiority over most state-of-the-art approaches.

通过提出一种名为 A2B-IS 的新框架，结合高斯骨架图和半监督学习策略，我们解决了显微镜图像分析中实例分割的一个基本挑战，使得模型在处理群集区域，连接的多个不同大小和形状的目标以及任意方向重叠的情况下能够获得更高的准确度。