Grading of cancer offers crucial insights for treatment planning. Morphology of glands in histology images is of prime importance for grading several types of cancers. Therefore, accurate segmentation of glands plays a pivotal role in planning the treatment in case of such cancers. We introduce a first-of-its-kind detail preserving conditional random field for gland segmentation from histology images. Our design involves a novel formulation of Gibbs energy that captures the spatial interaction between neighboring pixels through the hidden state of a 2-D recurrent neural network (2-D RNN). We show that the iterative training of the 2-D RNN results in the minimization of the Gibbs energy leading to accurate gland segmentation. Experiments on publicly available histology image datasets show the efficacy of the proposed method in accurate gland segmentation. Our model achieves at least 7% improvement in terms of Hausdorff distance for gland segmentation compared to a number of state-of-the-art techniques.

癌症分级为治疗计划提供了重要的见解。组织学图像中腺体的形态对于几种癌症的分级至关重要。因此，腺体的准确分割在此类癌症的治疗计划中起着关键作用。我们引入了一种首创的细节保留条件随机场，用于从组织学图像中进行腺体分割。我们的设计涉及一种新的吉布斯能量公式，它通过二维循环神经网络 (2-D RNN) 的隐藏状态捕获相邻像素之间的空间交互。我们表明，2-D RNN 的迭代训练导致吉布斯能量最小化，从而导致准确的腺体分割。在公开可用的组织学图像数据集上进行的实验显示了所提出的方法在准确腺体分割中的有效性。与许多最先进的技术相比，我们的模型在腺体分割的 Hausdorff 距离方面至少提高了 7%。