CT colonography: external clinical validation of an algorithm for computer-assisted prone and supine registration

Automatic Detection and Segmentation of Colorectal Cancer with Deep Residual Convolutional Neural Network

Early and automatic detection of colorectal tumors is essential for cancer analysis, and the same is implemented using computer-aided diagnosis (CAD). A computerized tomography (CT) image of the colon is being used to identify colorectal carcinoma. Digital imaging and communication in medicine (DICOM) is a standard medical imaging format to process and analyze images digitally. Accurate detection of tumor cells in the complex digestive tract is necessary for optimal treatment. The proposed work is divided into two phases. The first phase involves the segmentation, and the second phase is the extraction of the colon lesions with the observed segmentation parameters. A deep convolutional neural network (DCNN) based residual network approach for the colon and polyps' segmentation from the CT images is applied over the 2D CT images. The residual stack block is being added to the hidden layers with short skip nuance, which helps to retain spatial information. ResNet-enabled CNN is employed in the current work to achieve complete boundary segmentation of the colon cancer region. The results obtained through segmentation serve as features for further extraction and classification of benign as well as malignant colon cancer. Performance evaluation metrics indicate that the proposed network model has effectively segmented and classified colorectal tumors with dice scores of 91.57% (on average), sensitivity = 98.28, specificity = 98.68, and accuracy = 98.82.

From clinical imaging and computational models to personalised medicine and image guided interventions

This short paper describes the development of the UCL Centre for Medical Image Computing (CMIC) from 2006 to 2016, together with reference to historical developments of the Computational Imaging sciences Group (CISG) at Guy's Hospital. Key early work in automated image registration led to developments in image guided surgery and improved cancer diagnosis and therapy. The work is illustrated with examples from neurosurgery, laparoscopic liver and gastric surgery, diagnosis and treatment of prostate cancer and breast cancer, and image guided radiotherapy for lung cancer.

Progress in Fully Automated Abdominal CT Interpretation

OBJECTIVE

Automated analysis of abdominal CT has advanced markedly over just the last few years. Fully automated assessment of organs, lymph nodes, adipose tissue, muscle, bowel, spine, and tumors are some examples where tremendous progress has been made. Computer-aided detection of lesions has also improved dramatically.

CONCLUSION

This article reviews the progress and provides insights into what is in store in the near future for automated analysis for abdominal CT, ultimately leading to fully automated interpretation.

Effect of Reducing Abdominal Compression during Prone CT Colonography on Ascending Colonic Rotation during Supine-to-Prone Positional Change

Objective

To determine the effect of reduced abdominal compression in prone position on ascending colonic movement during supine-to-prone positional change during CT colonography (CTC).

Materials and Methods

Eighteen consecutive patients who had undergone prone CTC scanning with cushion blocks placed under the chest and hip/thigh to reduce abdominal compression and had confirmed sessile polyps ≥ 6 mm in the well-distended, straight, mid-ascending colon, were included. Radial location along the ascending colonic luminal circumference (°) was measured for 24 polyps and 54 colonic teniae on supine and prone CTC images. The supine-to-prone change ranging between -180° and +180° (- and + for internal and external colonic rotations, respectively), was determined. In addition, possible causes of any ascending colonic rotations were explored.

Results

Abdominal compression during prone CTC scanning completely disappeared with the use of cushion blocks in 17 of 18 patients. However, some degrees of ascending colonic rotation were still observed, with the radial location changes of -22° to 61° (median, 13.9°) for the polyps and similar degrees for teniae. Fifty-four percent and 56% of polyps and teniae, respectively, showed changes > 10°. The radial location change of the polyps was significantly associated with the degree of anterior shift of the small bowel and mesentery (r = 0.722, p < 0.001) and the degree of posterior displacement of the ascending colon (r = 0.566, p = 0.004) during supine-to-prone positional change.

Conclusion

Ascending colonic rotation upon supine-to-prone positional change during CTC, mostly in the form of external rotation, is not eliminated by removing abdominal compression in prone position.

CT colonography: clinical evaluation of a method for automatic coregistration of polyps at follow-up surveillance studies

PURPOSE

To evaluate the accuracy of a method of automatic coregistration of the endoluminal surfaces at computed tomographic (CT) colonography performed on separate occasions to facilitate identification of polyps in patients undergoing polyp surveillance.

MATERIALS AND METHODS

Institutional review board and HIPAA approval were obtained. A registration algorithm that was designed to coregister the coordinates of endoluminal colonic surfaces on images from prone and supine CT colonographic acquisitions was used to match polyps in sequential studies in patients undergoing polyp surveillance. Initial and follow-up CT colonographic examinations in 26 patients (35 polyps) were selected and the algorithm was tested by means of two methods, the longitudinal method (polyp coordinates from the initial prone and supine acquisitions were used to identify the expected polyp location automatically at follow-up CT colonography) and the consistency method (polyp coordinates from the initial supine acquisition were used to identify polyp location on images from the initial prone acquisition, then on those for follow-up prone and follow-up supine acquisitions). Two observers measured the Euclidean distance between true and expected polyp locations, and mean per-patient registration accuracy was calculated. Segments with and without collapse were compared by using the Kruskal-Wallace test, and the relationship between registration error and temporal separation was investigated by using the Pearson correlation.

RESULTS

Coregistration was achieved for all 35 polyps by using both longitudinal and consistency methods. Mean ± standard deviation Euclidean registration error for the longitudinal method was 17.4 mm ± 12.1 and for the consistency method, 26.9 mm ± 20.8. There was no significant difference between these results and the registration error when prone and supine acquisitions in the same study were compared (16.9 mm ± 17.6; P = .451).

CONCLUSION

Automatic endoluminal coregistration by using an algorithm at initial CT colonography allowed prediction of endoluminal polyp location at subsequent CT colonography, thereby facilitating detection of known polyps in patients undergoing CT colonographic surveillance.