Artificial Intelligence Imaging Diagnosis Using Super-Resolution and 3D Shape for Lymph Node Metastasis of Low Rectal Cancer: A Pilot Study From a Single Center.

BACKGROUND: Although accurate preoperative diagnosis of lymph node metastasis is essential for optimizing treatment strategies for low rectal cancer, the accuracy of present diagnostic modalities has room for improvement. OBJECTIVE: To establish a high-precision diagnostic method for lymph node metastasis of low rectal cancer using artificial intelligence. DESIGN: A retrospective observational study. SETTINGS: A single cancer center and a college of engineering in Japan. PATIENTS: Patients with low rectal adenocarcinoma who underwent proctectomy, bilateral lateral pelvic lymph node dissection, and contrast-enhanced multi-detector row computed tomography (slice ≤1 mm) between July 2015 and August 2021 were included in the present study. All pelvic lymph nodes from the aortic bifurcation to the upper edge of the anal canal were extracted, regardless of whether within or beyond the total mesenteric excision area, and pathological diagnoses were annotated for training and validation. MAIN OUTCOME MEASURES: Sensitivity, specificity, positive predictive value, negative predictive value, and accuracy. RESULTS: A total of 596 pathologically negative and 43 positive nodes from 52 patients were extracted and annotated. Four diagnostic methods, with and without using super-resolution images and without using 3D shape data, were performed and compared. The super-resolution + 3D shape data method had the best diagnostic ability for the combination of sensitivity, negative predictive value, and accuracy (0.964, 0.966, and 0.968, respectively), while the super-resolution only method had the best diagnostic ability for the combination of specificity and positive predictive value (0.994 and 0.993, respectively). LIMITATIONS: Small number of patients at a single center and the lack of external validation. CONCLUSIONS: Our results enlightened the potential of artificial intelligence for the method to become another game changer in the diagnosis and treatment of low rectal cancer. See Video Abstract.

A Vascular Feature Detection and Matching Method Based on Dual-Branch Fusion and Structure Enhancement.

How to obtain internal cavity features and perform image matching is a great challenge for laparoscopic 3D reconstruction. This paper proposes a method for detecting and associating vascular features based on dual-branch weighted fusion vascular structure enhancement. Our proposed method is divided into three stages, including analyzing various types of minimally invasive surgery (MIS) images and designing a universal preprocessing framework to make our method generalized. We propose a Gaussian weighted fusion vascular structure enhancement algorithm using the dual-branch Frangi measure and MFAT (multiscale fractional anisotropic tensor) to address the structural measurement differences and uneven responses between venous vessels and microvessels, providing effective structural information for vascular feature extraction. We extract vascular features through dual-circle detection based on branch point characteristics, and introduce NMS (non-maximum suppression) to reduce feature point redundancy. We also calculate the ZSSD (zero sum of squared differences) and perform feature matching on the neighboring blocks of feature points extracted from the front and back frames. The experimental results show that the proposed method has an average accuracy and repeatability score of 0.7149 and 0.5612 in the Vivo data set, respectively. By evaluating the quantity, repeatability, and accuracy of feature detection, our method has more advantages and robustness than the existing methods.

Lymph node detection in CT scans using modified U-Net with residual learning and 3D deep network.

PURPOSE: Lymph node (LN) detection is a crucial step that complements the diagnosis and treatments involved during cancer investigations. However, the low-contrast structures in the CT scan images and the nodes' varied shapes, sizes, and poses, along with their sparsely distributed locations, make the detection step challenging and lead to many false positives. The manual examination of the CT scan slices could be time-consuming, and false positives could divert the clinician's focus. To overcome these issues, our work aims at providing an automated framework for LNs detection in order to obtain more accurate detection results with low false positives. METHODS: The proposed work consists of two stages: candidate generation and false positive reduction. The first stage generates volumes of interest (VOI) of probable LN candidates using a modified U-Net with ResNet architecture to obtain high sensitivity but with the cost of increased false positives. The second-stage processes the obtained candidate LNs for false positive reduction using 3D convolutional neural network (CNN) classifier. We further present an analysis of various deep learning models while decomposing 3D VOI into different representations. RESULTS: The method is evaluated on two publicly available datasets containing CT scans of mediastinal and abdominal LNs. Our proposed approach yields sensitivities of 87% at 2.75 false positives per volume (FP/vol.) and 79% at 1.74 FP/vol. with the mediastinal and abdominal datasets, respectively. Our method presented a competitive performance in terms of sensitivity compared to the state-of-the-art methods and encountered very few false positives. CONCLUSION: We developed an automated framework for LNs detection using a modified U-Net with residual learning and 3D CNNs. The results indicate that our method could achieve high sensitivity with relatively low false positives, which helps avoid ineffective treatments.

Recovering 3D Shape with Absolute Size from Endoscope Images Using RBF Neural Network.

Medical diagnosis judges the status of polyp from the size and the 3D shape of the polyp from its medical endoscope image. However the medical doctor judges the status empirically from the endoscope image and more accurate 3D shape recovery from its 2D image has been demanded to support this judgment. As a method to recover 3D shape with high speed, VBW (Vogel-Breuß-Weickert) model is proposed to recover 3D shape under the condition of point light source illumination and perspective projection. However, VBW model recovers the relative shape but there is a problem that the shape cannot be recovered with the exact size. Here, shape modification is introduced to recover the exact shape with modification from that with VBW model. RBF-NN is introduced for the mapping between input and output. Input is given as the output of gradient parameters of VBW model for the generated sphere. Output is given as the true gradient parameters of true values of the generated sphere. Learning mapping with NN can modify the gradient and the depth can be recovered according to the modified gradient parameters. Performance of the proposed approach is confirmed via computer simulation and real experiment.