Segmentation-by-Detection: A Cascade Network for Volumetric Medical Image Segmentation

We propose an attention mechanism for 3D medical image segmentation. The method, named segmentation-by-detection, is a cascade of a detection module followed by a segmentation module. The detection module enables a region of interest to come to attention and produces a set of object region candidates which are further used as an attention model. Rather than dealing with the entire volume, the segmentation module distills the information from the potential region. This scheme is an efficient solution for volumetric data as it reduces the influence of the surrounding noise which is especially important for medical data with low signal-to-noise ratio. Experimental results on 3D ultrasound data of the femoral head shows superiority of the proposed method when compared with a standard fully convolutional network like the U-Net

Spectrum of injuries associated with paediatric ACL tears: an MRI pictorial review

OBJECTIVE: Magnetic resonance imaging (MRI) findings in anterior cruciate ligament (ACL) injury are well known, but most published reviews show obvious examples of associated injuries and give little focus to paediatric patients. Here, we demonstrate the spectrum of MRI appearances at common sites of associated injury in adolescents with ACL tears, emphasising age-specific issues. METHODS: Pictorial review using images from children with surgically confirmed ACL tears after athletic injury. RESULTS: ACL injury usually occurs with axial rotation in the valgus near full extension. The MRI findings can be obvious and important to management (ACL rupture), subtle but clinically important (lateral meniscus posterior attachment avulsion), obvious and unimportant to management (femoral condyle impaction injury), or subtle and possibly important (medial meniscocapsular junction tear). Paediatric-specific issues of note include tibial spine avulsion, normal difficulty visualising a thin ACL and posterolateral corner structures, and differentiation between incompletely closed physis and impaction fracture. CONCLUSION: ACL tear is only the most obvious sign of a complex injury involving multiple structures. Awareness of the spectrum of secondary findings illustrated here and the features distinguishing them from normal variation can aid in accurate assessment of ACL tears and related injuries, enabling effective treatment planning and assessment of prognosis. TEACHING POINTS: • The ACL in children normally appears thin or attenuated, while thickening and oedema suggest tear. • Displaced medial meniscal tears are significantly more common later post-injury than immediately. • The meniscofemoral ligaments merge with the posterior lateral meniscus, complicating tear assessment. • Tibial plateau impaction fractures can be difficult to distinguish from a partially closed physis. • Axial MR sequences are more sensitive/specific than coronal for diagnosis of medial collateral ligament (MCL) injury

End-to-end detection-segmentation network with ROI convolution

We propose an end-to-end neural network that improves the segmentation accuracy of fully convolutional networks by incorporating a localization unit. This network performs object localization first, which is then used as a cue to guide the training of the segmentation network. We test the proposed method on a segmentation task of small objects on a clinical dataset of ultrasound images. We show that by jointly learning for detection and segmentation, the proposed network is able to improve the segmentation accuracy compared to only learning for segmentation. Code is publicly available at https://github.com/vincentzhang/roi-fcn.Comment: ISBI 201

Incidence and Significance of Inconclusive Results in Ultrasound for Appendicitis in Children and Teenagers

AbstractPurposeFrustratingly, sonography to assess for appendicitis in children often leads to an inconclusive report (eg, “suspicious for appendicitis”) or nonvisualization of the appendix. To aid in planning who to image and how to interpret the results, we investigated whether these 2 results were more frequent in teenagers than preteens and the prevalence of appendicitis associated with each result.MethodsWe retrospectively reviewed sonographic and surgical findings in patients <18 years (n = 189) referred with clinical suspicion of appendicitis over a 12-month period. Children (≤12.0 years old; n = 86) and teens (>12.0 years old; n = 103) were compared.ResultsPrevalence of appendicitis was 34% in each group, similar to other centres; 0% for those with negative ultrasound reports (0/35), 10% for nonvisualized appendix (8/84), 68% for inconclusive report (15/22), and 85% for positive ultrasound (41/48). Teens were significantly more likely to have an inconclusive ultrasound. Inconclusive reports were because of borderline findings (eg, appendix size near 6 mm; 9/22), body habitus, bowel gas, or unusual findings due in retrospect to perforation. The rate of nonvisualization of the appendix did not vary significantly with age (42% vs 47%).ConclusionAn inconclusive result of ultrasound for appendicitis was significantly more frequent in teens than in preteens and carried a high (68%) likelihood of appendicitis. Conversely, a nonvisualized appendix was equally frequent in teens and preteens, and had a low likelihood of appendicitis (only 10% positive). These findings encourage the use of ultrasound in preteens in particular and can assist interpretation of these common results

MRI of the axial skeleton in spondyloarthritis : the many faces of new bone formation

Spondyloarthritis has two hallmark features: active inflammation and structural lesions with new bone formation. MRI is well suited to assess active inflammation, but there is increasing interest in the role of structural lesions at MRI. Recent MRI studies have examined the established features of new bone formation and demonstrated some novel features which show diagnostic value and might even have potential as possible markers of disease progression. Although MRI is not the first imaging modality that comes into mind for assessment of bony changes, these features of new bone formation can be detected on MRI-if one knows how to recognize them. This review illustrates the MRI features of new bone formation and addresses possible pitfalls

Self-Supervised-RCNN for Medical Image Segmentation with Limited Data Annotation

Many successful methods developed for medical image analysis that are based on machine learning use supervised learning approaches, which often require large datasets annotated by experts to achieve high accuracy. However, medical data annotation is time-consuming and expensive, especially for segmentation tasks. To solve the problem of learning with limited labeled medical image data, an alternative deep learning training strategy based on self-supervised pretraining on unlabeled MRI scans is proposed in this work. Our pretraining approach first, randomly applies different distortions to random areas of unlabeled images and then predicts the type of distortions and loss of information. To this aim, an improved version of Mask-RCNN architecture has been adapted to localize the distortion location and recover the original image pixels. The effectiveness of the proposed method for segmentation tasks in different pre-training and fine-tuning scenarios is evaluated based on the Osteoarthritis Initiative dataset. Using this self-supervised pretraining method improved the Dice score by 20% compared to training from scratch. The proposed self-supervised learning is simple, effective, and suitable for different ranges of medical image analysis tasks including anomaly detection, segmentation, and classification

Weakly Supervised Medical Image Segmentation With Soft Labels and Noise Robust Loss

Recent advances in deep learning algorithms have led to significant benefits for solving many medical image analysis problems. Training deep learning models commonly requires large datasets with expert-labeled annotations. However, acquiring expert-labeled annotation is not only expensive but also is subjective, error-prone, and inter-/intra- observer variability introduces noise to labels. This is particularly a problem when using deep learning models for segmenting medical images due to the ambiguous anatomical boundaries. Image-based medical diagnosis tools using deep learning models trained with incorrect segmentation labels can lead to false diagnoses and treatment suggestions. Multi-rater annotations might be better suited to train deep learning models with small training sets compared to single-rater annotations. The aim of this paper was to develop and evaluate a method to generate probabilistic labels based on multi-rater annotations and anatomical knowledge of the lesion features in MRI and a method to train segmentation models using probabilistic labels using normalized active-passive loss as a "noise-tolerant loss" function. The model was evaluated by comparing it to binary ground truth for 17 knees MRI scans for clinical segmentation and detection of bone marrow lesions (BML). The proposed method successfully improved precision 14, recall 22, and Dice score 8 percent compared to a binary cross-entropy loss function. Overall, the results of this work suggest that the proposed normalized active-passive loss using soft labels successfully mitigated the effects of noisy labels

Diagnostic performance for erosion detection in sacroiliac joints on MR T1-weighted images : comparison between different slice thicknesses

Purpose: To assess the effect of slice thickness on the diagnostic accuracy of erosion detection at MR T1-weighted images (T1WI) of the sacroiliac joints (SIJ) in adult patients suspected of sacroiliitis. Method: Patients aged 18-60 years with clinical suspicion of sacroiliitis were enrolled. All patients underwent CT and 3 T MRI of the SIJs on the same day. CT at 1 mm slice thickness, semi-coronal spin echo T1WI sequences with four different slice thicknesses (2, 3, 4 and 5 mm) were obtained. For scoring erosions, each SIJ was divided into four quadrants. Presence or absence of erosions was scored on T1WI sequences by two independent readers blinded to other data. Inter-reader agreement was assessed using kappa statistics. Diagnostic accuracy of MRI for erosions at each slice thickness was evaluated vs. consensus CT as reference standard, using area under the receiver operating characteristic curve (AUC). Results: Fifty-three patients (23 men, 30 women, mean age, 39.0 years +/- 10.2) were included. Inter-reader agreement for erosion score on all T1WI sequences was moderate (kappa value 0.54 to 0.60). With increasing slice thickness, both the recorded total number of erosions and sensitivity for erosion vs. CT decreased. The AUC were significantly higher for 2 mm and 3 mm T1WI than for 4 mm and 5 mm T1WI. Conclusions: The diagnostic accuracy of T1WI for erosion detection vs. a CT reference standard is affected by slice thickness. Thinner slices (2 or 3 mm) had significantly higher diagnostic accuracy than thicker slices (4 or 5 mm)

Concurrent validity and reliability of a semi-automated approach to measuring the magnetic resonance imaging morphology of the knee joint in active youth

Post-traumatic knee osteoarthritis is attributed to alterations in joint morphology, alignment, and biomechanics triggered by injury. While magnetic resonance (MR) imaging-based measures of joint morphology and alignment are relevant to understanding osteoarthritis risk, time consuming manual data extraction and measurement limit the number of outcomes that can be considered and deter widespread use. This paper describes the development and evaluation of a semi-automated software for measuring tibiofemoral and patellofemoral joint architecture using MR images from youth with and without a previous sport-related knee injury. After prompting users to identify and select key anatomical landmarks, the software can calculate 37 (14 tibiofemoral, 23 patellofemoral) relevant geometric features (morphology and alignment) based on established methods. To assess validity and reliability, 11 common geometric features were calculated from the knee MR images (proton density and proton density fat saturation sequences; 1.5 T) of 76 individuals with a 3-10-year history of youth sport-related knee injury and 76 uninjured controls. Spearman's or Pearson's correlation coefficients (95% CI) and Bland-Altman plots were used to assess the concurrent validity of the semi-automated software (novice rater) versus expert manual measurements, while intra-class correlation coefficients (ICC 2,1; 95%CI), standard error of measurement (95%CI), 95% minimal detectable change, and Bland-Altman plots were used to assess the inter-rater reliability of the semi-automated software (novice vs resident radiologist rater). Correlation coefficients ranged between 0.89 (0.84, 0.92; Lateral Trochlear Inclination) and 0.97 (0.96, 0.98; Patellar Tilt Angle). ICC estimates ranged between 0.79 (0.63, 0.88; Lateral Patellar Tilt Angle) and 0.98 (0.95, 0.99; Bisect Offset). Bland-Altman plots did not reveal systematic bias. These measurement properties estimates are equal, if not better than previously reported methods suggesting that this novel semi-automated software is an accurate, reliable, and efficient alternative method for measuring large numbers of geometric features of the tibiofemoral and patellofemoral joints from MR studies. </p