Deshmukh Gayatri, Susladkar Onkar, Makwana Dhruv, Chandra Teja R Sai, Kumar S Nagesh, Mittal Sparsh
Vishwakarma Institute of Information Technology, Pune, India.
Independent researchers.
Phys Med Biol. 2022 Sep 28;67(19). doi: 10.1088/1361-6560/ac8594.
Automated cell nuclei segmentation is vital for the histopathological diagnosis of cancer. However, nuclei segmentation from 'hematoxylin and eosin' (HE) stained 'whole slide images' (WSIs) remains a challenge due to noise-induced intensity variations and uneven staining. The goal of this paper is to propose a novel deep learning model for accurately segmenting the nuclei in HE-stained WSIs.We introduce FEEDNet, a novel encoder-decoder network that uses LSTM units and 'feature enhancement blocks' (FE-blocks). Our proposed FE-block avoids the loss of location information incurred by pooling layers by concatenating the downsampled version of the original image to preserve pixel intensities. FEEDNet uses an LSTM unit to capture multi-channel representations compactly. Secondly, for datasets that provide class information, we train a multiclass segmentation model, which generates masks corresponding to each class at the output. Using this information, we generate more accurate binary masks than that generated by conventional binary segmentation models.We have thoroughly evaluated FEEDNet on CoNSeP, Kumar, and CPM-17 datasets. FEEDNet achieves the best value of PQ (panoptic quality) on CoNSeP and CPM-17 datasets and the second best value of PQ on the Kumar dataset. The 32-bit floating-point version of FEEDNet has a model size of 64.90 MB. With INT8 quantization, the model size reduces to only 16.51 MB, with a negligible loss in predictive performance on Kumar and CPM-17 datasets and a minor loss on the CoNSeP dataset.Our proposed idea of generalized class-aware binary segmentation is shown to be accurate on a variety of datasets. FEEDNet has a smaller model size than the previous nuclei segmentation networks, which makes it suitable for execution on memory-constrained edge devices. The state-of-the-art predictive performance of FEEDNet makes it the most preferred network. The source code can be obtained fromhttps://github.com/CandleLabAI/FEEDNet.
自动细胞核分割对于癌症的组织病理学诊断至关重要。然而，由于噪声引起的强度变化和染色不均，从苏木精和伊红（HE）染色的全切片图像（WSI）中进行细胞核分割仍然是一项挑战。本文的目标是提出一种新颖的深度学习模型，用于准确分割HE染色WSI中的细胞核。我们引入了FEEDNet，这是一种新颖的编码器-解码器网络，它使用长短期记忆（LSTM）单元和特征增强块（FE-block）。我们提出的FE-block通过连接原始图像的下采样版本以保留像素强度，避免了池化层导致的位置信息丢失。FEEDNet使用LSTM单元紧凑地捕获多通道表示。其次，对于提供类别信息的数据集，我们训练了一个多类别分割模型，该模型在输出端生成与每个类别对应的掩码。利用这些信息，我们生成的二值掩码比传统二值分割模型生成的更准确。我们在CoNSeP、Kumar和CPM-17数据集上对FEEDNet进行了全面评估。FEEDNet在CoNSeP和CPM-17数据集上实现了全景质量（PQ）的最佳值，在Kumar数据集上实现了PQ的第二最佳值。FEEDNet的32位浮点版本的模型大小为64.90MB。通过INT8量化，模型大小仅减少到16.51MB，在Kumar和CPM-17数据集上的预测性能损失可忽略不计，在CoNSeP数据集上有轻微损失。我们提出的广义类别感知二值分割思想在各种数据集上都被证明是准确的。FEEDNet的模型大小比以前的细胞核分割网络更小，这使其适合在内存受限的边缘设备上执行。FEEDNet的先进预测性能使其成为最受欢迎的网络。源代码可从https://github.com/CandleLabAI/FEEDNet获得。
