Adaptive Segmentation of Knee Radiographs for Selecting the Optimal ROI in Texture Analysis

The purposes of this study were to investigate: 1) the effect of placement of region-of-interest (ROI) for texture analysis of subchondral bone in knee radiographs, and 2) the ability of several texture descriptors to distinguish between the knees with and without radiographic osteoarthritis (OA). Bilateral posterior-anterior knee radiographs were analyzed from the baseline of OAI and MOST datasets. A fully automatic method to locate the most informative region from subchondral bone using adaptive segmentation was developed. We used an oversegmentation strategy for partitioning knee images into the compact regions that follow natural texture boundaries. LBP, Fractal Dimension (FD), Haralick features, Shannon entropy, and HOG methods were computed within the standard ROI and within the proposed adaptive ROIs. Subsequently, we built logistic regression models to identify and compare the performances of each texture descriptor and each ROI placement method using 5-fold cross validation setting. Importantly, we also investigated the generalizability of our approach by training the models on OAI and testing them on MOST dataset.We used area under the receiver operating characteristic (ROC) curve (AUC) and average precision (AP) obtained from the precision-recall (PR) curve to compare the results. We found that the adaptive ROI improves the classification performance (OA vs. non-OA) over the commonly used standard ROI (up to 9% percent increase in AUC). We also observed that, from all texture parameters, LBP yielded the best performance in all settings with the best AUC of 0.840 [0.825, 0.852] and associated AP of 0.804 [0.786, 0.820]. Compared to the current state-of-the-art approaches, our results suggest that the proposed adaptive ROI approach in texture analysis of subchondral bone can increase the diagnostic performance for detecting the presence of radiographic OA

Evaluation of the effect of multi-dose Methotrexate therapy on ovarian reserve in ectopic pregnancies: Is polycystic ovarian morphology a protective condition for ovarian reserve?

Objectives: The study aims to evaluate the effects of multi-dose methotrexate (MTX) or subsequent salpingectomy on ovarian reserve and explain the conditions that cause the change in serum anti-Müllerian hormone (AMH) levels. Material and methods: Our department had 58 tubal EP patients treated with a multiple-dose MTX protocol or subsequent salpingectomy between 2017–2020. Serum AMH level was measured in each patient before the medication and 3–6 months after therapy. Patients' details were recorded and analyzed later. Results: The mean AMH value decreased in 32 patients (−17.8%), increased in 26 patients (+31.5%) (p < 0.0001). In the group with an increase, there was a rather high number of patients with a polycystic ovary (PCO) condition compared to the other group (p = 0.0001). AMH values increased in patients with PCO and decreased in patients having no PCO (p < 0.001). Conclusions: Multiple-dose MTX or subsequent salpingectomy treatment in tubal ectopic pregnancy (EP) patients might not refer to significant differences in patients' AMH levels. Remarkably, post-treatment AMH levels were significantly increased in EP patients with PCO and decreased in those without this condition. PCO may be a protective condition for ovarian reserve

Comparison of 3D local and global descriptors for similarity retrieval of range data

Recent improvements in scanning technologies such as consumer penetration of RGB-D cameras lead obtaining and managing range image databases practical. Hence, the need for describing and indexing such data arises. In this study, we focus on similarity indexing of range data among a database of range objects (range-to-range retrieval) by employing only single view depth information. We utilize feature based approaches both on local and global scales. However, the emphasis is on the local descriptors with their global representations. A comparative study with extensive experimental results is presented. In addition, we introduce a publicly available range object database which is large and has a high diversity that is suitable for similarity retrieval applications. The simulation results indicate competitive performance between local and global methods. While better complexity trade-off can be achieved with the global techniques, local methods perform better in distinguishing different parts of incomplete depth data

Segmentation Driven Semantic Information Inference from 2.5D Data

In this work, we propose a multi-relational concept discovery method for business intelligence applications. Multi-relational data mining finds interesting patterns that span over multiple tables. The obtained patterns reveal useful information for decision making in business environments. However as the patterns include multiple relations, the search space gets intractably complex. In order to cope with this problem, various search strategies, heuristics and language pattern limitations are employed in multi-relational learning systems. In this work, we develop an ILP-based concept discovery method that uses inverse resolution for generalization of concept instances in the presence of background knowledge and refines these patterns into concept definitions by applying specialization operator There are two main benefits in this appoach. The first one is to relax the strong declarative biases and user-defined specifications. The second one is to integrate the method on relational databases so that usage of the system is facilitated in business intelligence applications.Semantic information retrieval from unorganized point clouds becomes necessity for incoming technology such as 3DTV. Besides we surrounded with planar, nearly planar and partially planar things. With this motivation we aim to find planar structures in 2.5D point clouds. With the Hough Transform found in literature, Recursive Hough Transform and Hough Trasform with segmentation algorithms, which are variations of the original algorithm obtained by us, are implemented. K-Means and Mean-shift algorithms, which are popular segmentation methods in 2D, are adapted to 3D with/without color information and their performance analysis are presented

Lossless Description of 3D Range Models

The improvements in scanning technologies lead obtaining and managing range image databases. Hence, need of describing and indexing this type of data arises. Since a range model owns different properties compared to complete 3D models, we propose a method that relies on Spherical Harmonics Transform (SHT) for retrieving similar models where the query and the database both consist of only range models. Although SHT, is not a new concept in shape retrieval research, we propose to utilize it for range images by representing the models in a world seen from the camera. The difference and advantage of our algorithm is being information lossless. That is the available shape information is completely included in obtaining the descriptor whereas other mesh retrieval applications utilizing SHT "approximates" the shape that leads information loss. The descriptor is invariant to scale and rotations about z-axis. Proposed method is tested on a large database having high diversity. Performance of the proposed method is superior to the performance of the D2 distribution

INTEGRATION OF 2D IMAGES AND RANGE DATA FOR OBJECT SEGMENTATION AND RECOGNITION

In the field of vision based robot actuation, in order to manipulate objects in an environment, background separation and object selection a re fundamental tasks that should be carried out in a fast and efficient way. In this paper, we propose a method to segment possible object locations in the scene and recognize them via local-point based representation. Exploiting the resulting 3D structure of the scene via a time-of-flight camera, background regions are eliminated with the assumption that the objects are placed on planar surfaces. Next, object recognition is performed using scale invariant features in the captured high resolution images via standard camera. The preliminary experimental results show that the proposed system gives promising results for background segmentation and object recognition, especially for the service robot environments, which could also be utilized as a pre-processing step in path planning and 3D scene map generation

Deep learning for magnification independent breast cancer histopathology image classification

Abstract Microscopic analysis of breast tissues is necessary for a definitive diagnosis of breast cancer which is the most common cancer among women. Pathology examination requires time consuming scanning through tissue images under different magnification levels to find clinical assessment clues to produce correct diagnoses. Advances in digital imaging techniques offers assessment of pathology images using computer vision and machine learning methods which could automate some of the tasks in the diagnostic pathology workflow. Such automation could be beneficial to obtain fast and precise quantification, reduce observer variability, and increase objectivity. In this work, we propose to classify breast cancer histopathology images independent of their magnifications using convolutional neural networks (CNNs). We propose two different architectures; single task CNN is used to predict malignancy and multi-task CNN is used to predict both malignancy and image magnification level simultaneously. Evaluations and comparisons with previous results are carried out on BreaKHis dataset. Experimental results show that our magnification independent CNN approach improved the performance of magnification specific model. Our results in this limited set of training data are comparable with previous state-of-the-art results obtained by hand-crafted features. However, unlike previous methods, our approach has potential to directly benefit from additional training data, and such additional data could be captured with same or different magnification levels than previous data

UTILIZATION OF SPATIAL INFORMATION FOR POINT CLOUD SEGMENTATION

Object segmentation has an important role in the field of computer vision for semantic information inference. Many applications such as 3DTV archive systems, 3D/2D model fitting, object recognition and shape retrieval are strongly dependent to the performance of the segmentation process. In this paper we present a new algorithm for object localization and segmentation based on the spatial information obtained via a Time-of-Flight (TOF) camera. 3D points obtained via a TOF camera are projected onto the major plane representing the planar surface on which the objects are placed. Afterward, the most probable regions that an item can be placed are extracted by using kernel density estimation method and 3D points are segmented into objects. Also some well-known segmentation algorithms are tested on the 3D (depth) images

Deep Learning for Predicting Progression of Patellofemoral Osteoarthritis Based on Lateral Knee Radiographs, Demographic Data and Symptomatic Assessments

In this study, we propose a novel framework that utilizes deep learning (DL) and attention mechanisms to predict the radiographic progression of patellofemoral osteoarthritis (PFOA) over a period of seven years. This study included subjects (1832 subjects, 3276 knees) from the baseline of the MOST study. PF joint regions-of-interest were identified using an automated landmark detection tool (BoneFinder) on lateral knee X-rays. An end-to-end DL method was developed for predicting PFOA progression based on imaging data in a 5-fold cross-validation setting. A set of baselines based on known risk factors were developed and analyzed using gradient boosting machine (GBM). Risk factors included age, sex, BMI and WOMAC score, and the radiographic osteoarthritis stage of the tibiofemoral joint (KL score). Finally, we trained an ensemble model using both imaging and clinical data. Among the individual models, the performance of our deep convolutional neural network attention model achieved the best performance with an AUC of 0.856 and AP of 0.431; slightly outperforming the deep learning approach without attention (AUC=0.832, AP= 0.4) and the best performing reference GBM model (AUC=0.767, AP= 0.334). The inclusion of imaging data and clinical variables in an ensemble model allowed statistically more powerful prediction of PFOA progression (AUC = 0.865, AP=0.447), although the clinical significance of this minor performance gain remains unknown. This study demonstrated the potential of machine learning models to predict the progression of PFOA using imaging and clinical variables. These models could be used to identify patients who are at high risk of progression and prioritize them for new treatments. However, even though the accuracy of the models were excellent in this study using the MOST dataset, they should be still validated using external patient cohorts in the future