Tumor Segmentation in Intraoperative Fluorescence Images Based on Transfer Learning and Convolutional Neural Networks.

OBJECTIVE: To propose a transfer learning based method of tumor segmentation in intraoperative fluorescence images, which will assist surgeons to efficiently and accurately identify the boundary of tumors of interest. METHODS: We employed transfer learning and deep convolutional neural networks (DCNNs) for tumor segmentation. Specifically, we first pre-trained four networks on the ImageNet dataset to extract low-level features. Subsequently, we fine-tuned these networks on two fluorescence image datasets (ABFM and DTHP) separately to enhance the segmentation performance of fluorescence images. Finally, we tested the trained models on the DTHL dataset. The performance of this approach was compared and evaluated against DCNNs trained end-to-end and the traditional level-set method. RESULTS: The transfer learning-based UNet++ model achieved high segmentation accuracies of 82.17% on the ABFM dataset, 95.61% on the DTHP dataset, and 85.49% on the DTHL test set. For the DTHP dataset, the pre-trained Deeplab v3 + network performed exceptionally well, with a segmentation accuracy of 96.48%. Furthermore, all models achieved segmentation accuracies of over 90% when dealing with the DTHP dataset. CONCLUSION: To the best of our knowledge, this study explores tumor segmentation on intraoperative fluorescent images for the first time. The results show that compared to traditional methods, deep learning has significant advantages in improving segmentation performance. Transfer learning enables deep learning models to perform better on small-sample fluorescence image data compared to end-to-end training. This discovery provides strong support for surgeons to obtain more reliable and accurate image segmentation results during surgery.

Automated peripancreatic vessel segmentation and labeling based on iterative trunk growth and weakly supervised mechanism.

Peripancreatic vessel segmentation and anatomical labeling are pivotal aspects in aiding surgical planning and prognosis for patients with pancreatic tumors. Nevertheless, prevailing techniques often fall short in achieving satisfactory segmentation performance for the peripancreatic vein (PPV), leading to predictions characterized by poor integrity and connectivity. Besides, unsupervised labeling algorithms usually cannot deal with complex anatomical variation while fully supervised methods require a large number of voxel-wise annotations for training, which is very labor-intensive and time-consuming. To address these two problems, we propose an Automated Peripancreatic vEssel Segmentation and lAbeling (APESA) framework, to not only highly improve the segmentation performance for PPV, but also efficiently identify the peripancreatic artery (PPA) branches. There are two core modules in our proposed APESA framework: iterative trunk growth module (ITGM) for vein segmentation and weakly supervised labeling mechanism (WSLM) for artery labeling. The ITGM is composed of a series of iterative submodules, each of which chooses the largest connected component of the previous PPV segmentation as the trunk of a tree structure, seeks for the potential missing branches around the trunk by our designed branch proposal network, and facilitates trunk growth under the connectivity constraint. The WSLM incorporates the rule-based pseudo label generation with less expert participation, an anatomical labeling network to learn the branch distribution voxel by voxel, and adaptive radius-based postprocessing to refine the branch structures of the labeling predictions. Our achieved Dice of 94.01% for PPV segmentation on our collected dataset represents an approximately 10% accuracy improvement compared to state-of-the-art methods. Additionally, we attained a Dice of 97.01% for PPA segmentation and competitive labeling performance for PPA labeling compared to prior works. Our source codes will be publicly available at https://github.com/ZouLiwen-1999/APESA.

Deep learning radiomics based prediction of axillary lymph node metastasis in breast cancer.

This study aimed to develop and validate a deep learning radiomics nomogram (DLRN) for the preoperative evaluation of axillary lymph node (ALN) metastasis status in patients with a newly diagnosed unifocal breast cancer. A total of 883 eligible patients with breast cancer who underwent preoperative breast and axillary ultrasound were retrospectively enrolled between April 1, 2016, and June 30, 2022. The training cohort comprised 621 patients from Hospital I; the external validation cohorts comprised 112, 87, and 63 patients from Hospitals II, III, and IV, respectively. A DLR signature was created based on the deep learning and handcrafted features, and the DLRN was then developed based on the signature and four independent clinical parameters. The DLRN exhibited good performance, yielding areas under the receiver operating characteristic curve (AUC) of 0.914, 0.929, and 0.952 in the three external validation cohorts, respectively. Decision curve and calibration curve analyses demonstrated the favorable clinical value and calibration of the nomogram. In addition, the DLRN outperformed five experienced radiologists in all cohorts. This has the potential to guide appropriate management of the axilla in patients with breast cancer, including avoiding overtreatment.

A Weakly Supervised Segmentation Network Embedding Cross-Scale Attention Guidance and Noise-Sensitive Constraint for Detecting Tertiary Lymphoid Structures of Pancreatic Tumors.

The presence of tertiary lymphoid structures (TLSs) on pancreatic pathological images is an important prognostic indicator of pancreatic tumors. Therefore, TLSs detection on pancreatic pathological images plays a crucial role in diagnosis and treatment for patients with pancreatic tumors. However, fully supervised detection algorithms based on deep learning usually require a large number of manual annotations, which is time-consuming and labor-intensive. In this paper, we aim to detect the TLSs in a manner of few-shot learning by proposing a weakly supervised segmentation network. We firstly obtain the lymphocyte density maps by combining a pretrained model for nuclei segmentation and a domain adversarial network for lymphocyte nuclei recognition. Then, we establish a cross-scale attention guidance mechanism by jointly learning the coarse-scale features from the original histopathology images and fine-scale features from our designed lymphocyte density attention. A noise-sensitive constraint is introduced by an embedding signed distance function loss in the training procedure to reduce tiny prediction errors. Experimental results on two collected datasets demonstrate that our proposed method significantly outperforms the state-of-the-art segmentation-based algorithms in terms of TLSs detection accuracy. Additionally, we apply our method to study the congruent relationship between the density of TLSs and peripancreatic vascular invasion and obtain some clinically statistical results.

CTG-Net: an efficient cascaded framework driven by terminal guidance mechanism for dilated pancreatic duct segmentation.

Pancreatic duct dilation indicates a high risk of various pancreatic diseases. Segmentation for dilated pancreatic duct (DPD) on computed tomography (CT) image shows the potential to assist the early diagnosis, surgical planning and prognosis. Because of the DPD's tiny size, slender tubular structure and the surrounding distractions, most current researches on DPD segmentation achieve low accuracy and always have segmentation errors on the terminal DPD regions. To address these problems, we propose a cascaded terminal guidance network to efficiently improve the DPD segmentation performance. Firstly, a basic cascaded segmentation architecture is established to get the pancreas and coarse DPD segmentation, a DPD graph structure is build on the coarse DPD segmentation to locate the terminal DPD regions. Then, a terminal anatomy attention module is introduced for jointly learning the local intensity from the CT images, feature cues from the coarse DPD segmentation and global anatomy information from the designed pancreas anatomy-aware maps. Finally, a terminal distraction attention module which explicitly learns the distribution of the terminal distraction regions is proposed to reduce the false positive and false negative predictions. We also propose a new metric called tDice to measure the terminal segmentation accuracy for targets with tubular structures and two other metrics for segmentation error evaluation. We collect our dilated pancreatic duct segmentation dataset with 150 CT scans from patients with five types of pancreatic tumors. Experimental results on our dataset show that our proposed approach boosts DPD segmentation accuracy by nearly 20% compared with the existing results, and achieves more than 9% improvement for the terminal segmentation accuracy compared with the state-of-the-art methods.