Advanced Faster RCNN: a non-contrast CT-based algorithm for detecting pancreatic lesions in multiple disease stages / 南方医科大学学报

OBJECTIVE@#To propose a non-contrast CT-based algorithm for automated and accurate detection of pancreatic lesions at a low cost.@*METHODS@#With Faster RCNN as the benchmark model, an advanced Faster RCNN (aFaster RCNN) model for pancreatic lesions detection based on plain CT was constructed. The model uses the residual connection network Resnet50 as the feature extraction module to extract the deep image features of pancreatic lesions. According to the morphology of pancreatic lesions, 9 anchor frame sizes were redesigned to construct the RPN module. A new Bounding Box regression loss function was proposed to constrain the training process of RPN module regression subnetwork by comprehensively considering the constraints of the lesion shape and anatomical structure. Finally, a detection frame was generated using the detector in the second stage. The data from a total of 728 cases of pancreatic diseases from 4 clinical centers in China were used for training (518 cases, 71.15%) and testing (210 cases, 28.85%) of the model. The performance of aFaster RCNN was verified through ablation experiments and comparison experiments with 3 classical target detection models SSD, YOLO and CenterNet.@*RESULTS@#The aFaster RCNN model for pancreatic lesion detection achieved recall rates of 73.64% at the image level and 92.38% at the patient level, with an average precision of 45.29% and 53.80% at the image and patient levels, respectively, which were higher than those of the 3 models for comparison.@*CONCLUSION@#The proposed method can effectively extract the imaging features of pancreatic lesions from non-contrast CT images to detect the pancreatic lesions.

External and Internal Validation of a Computer Assisted Diagnostic Model for Detecting Multi-Organ Mass Lesions in CT images / 中国医学科学杂志(英文版)

Objective We developed a universal lesion detector (ULDor) which showed good performance in in-lab experiments. The study aims to evaluate the performance and its ability to generalize in clinical setting via both external and internal validation. Methods The ULDor system consists of a convolutional neural network (CNN) trained on around 80K lesion annotations from about 12K CT studies in the DeepLesion dataset and 5 other public organ-specific datasets. During the validation process, the test sets include two parts: the external validation dataset which was comprised of 164 sets of non-contrasted chest and upper abdomen CT scans from a comprehensive hospital, and the internal validation dataset which was comprised of 187 sets of low-dose helical CT scans from the National Lung Screening Trial (NLST). We ran the model on the two test sets to output lesion detection. Three board-certified radiologists read the CT scans and verified the detection results of ULDor. We used positive predictive value (PPV) and sensitivity to evaluate the performance of the model in detecting space-occupying lesions at all extra-pulmonary organs visualized on CT images, including liver, kidney, pancreas, adrenal, spleen, esophagus, thyroid, lymph nodes, body wall, thoracic spine,

Probabilistic Multiple Sclerosis Lesion Detection using Superpixels and Markov Random Fields / Detección Probabilística de Lesiones de Esclerosis Múltiple usando Superpixeles y Campos Aleatorios de Markov

Abstract Multiple Sclerosis (MS) is the most common neurodegenerative disease among young adults. Diagnosis and monitoring of MS is performed with T2-weighted or T2 FLAIR magnetic resonance imaging, where MS lesions appear as hyperintense spots in the white matter. In recent years, multiple algorithms have been proposed to detect these lesions with varying success rates, which greatly depend on the amount of a priori information required by each algorithm, such as the use of an atlas or the involvement of an expert to guide the segmentation process. In this work, a fully automatic method that does not rely on a priori anatomical information is proposed and evaluated. The proposed algorithm is based on an over-segmentation in superpixels and their classification by means of Gauss-Markov Measure Fields (GMMF). The main advantage of the over-segmentation is that it preserves the borders between tissues, while the GMMF classifier is robust to noise and computationally efficient. The proposed segmentation is then applied in two stages: first to segment the brain region and then to detect hyperintense spots within the brain. The proposed method is evaluated with synthetic images from BrainWeb, as well as real images from MS patients. The proposed method produces competitive results with respect to other algorithms in the state of the art, without requiring user assistance nor anatomical prior information.

Resumen La Esclerosis Múltiple (MS) es una de las enfermedades neurodegenerativas más comunes en adultos jóvenes. El diagnóstico y su monitoreo se realiza generalmente mediante imágenes de resonancia magnética T2 o T2 FLAIR, donde se observan regiones hiperintensas relacionadas a lesiones cerebrales causadas por la MS. En años recientes, múltiples algoritmos han sido propuestos para detectar estas lesiones con diferentes tasas de éxito las cuales dependen en gran medida de la cantidad de información a priori que requiere cada algoritmo, como el uso de un atlas o el involucramiento de un experto que guíe el proceso de segmentación. En este trabajo, se propone un método automático independiente de información anatómica. El algoritmo propuesto está basado en una sobresegmentación en superpixeles y su clasificación mediante un proceso de Campos Aleatorios de Markov de Medidas Gaussianas (GMMF). La principal ventaja de la sobresegmentación es que preserva bordes entre tejidos, además que tiene un costo reducido en tiempo de ejecución, mientras que el clasificador GMMF es robusto a ruido y computacionalmente eficiente. La segmentación propuesta es aplicada en dos etapas: primero para segmentar el cerebro y después para detectar las lesiones en él. El método propuesto es evaluado usando imágenes sintéticas de BrainWeb, así como también imágenes reales de pacientes con MS. Con respecto a los resultados, el método propuesto muestra un desempeño competitivo respecto a otros métodos en el estado del arte, tomando en cuenta que éste no requiere de asistencia o información a priori.

Application of Artificial Intelligence in Capsule Endoscopy: Where Are We Now?

Unlike wired endoscopy, capsule endoscopy requires additional time for a clinical specialist to review the operation and examine the lesions. To reduce the tedious review time and increase the accuracy of medical examinations, various approaches have been reported based on artificial intelligence for computer-aided diagnosis. Recently, deep learning–based approaches have been applied to many possible areas, showing greatly improved performance, especially for image-based recognition and classification. By reviewing recent deep learning–based approaches for clinical applications, we present the current status and future direction of artificial intelligence for capsule endoscopy.